Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor whose ligand-induced conformational changes, primarily driven by helix 12 (H12) repositioning, regulate transcriptional activity. However, the precise mechanism remains elusive. In this study, we performed classical molecular dynamics (cMD) simulations of the PPARγ ligand binding domain (LBD) in complex with two agonists (BRL, 3EA), a partial agonist (GW0072), and an antagonist (EKP), generating 3 μs trajectories for each system. To gain deeper insights, we integrated machine learning-assisted clustering with MD simulations, revealing a favorable trend in binding free energy (ΔG b), suggesting enhanced complex stability. A case study on EKP demonstrated that, despite fitting within the binding site, it failed to induce rapid LBD or H12 rearrangements in the apo agonist-induced conformation. Additionally, we investigated the apo-state conformations of PPARγ-LBD influenced by agonist and antagonist ligands, utilizing cMD and Gaussian accelerated molecular dynamics (GaMD) over a cumulative 6 μs (3 μs cMD + 3 μs GaMD). Key residues known to modulate PPARγ function upon mutation were analyzed, and simulations confirmed the high stability of both apo and ligand-bound conformations. Notably, in the apo state, specific H12 residues interacted with other PPARγ-LBD regions, preventing disorder and abrupt transitions. These findings guided the selection of collective variables (CVs) for well-tempered metadynamics (WT-MetaD) simulations, which-in the apo-agonist state-captured the H12 shift from agonist- to antagonist-like conformations, consistent with resolved X-ray structures. Overall, this computational framework provides novel insights into PPARγ-LBD conformational dynamics and establishes a valuable approach for rationally assessing the effects of modulators on PPARγ activity.

In Silico Identification of Potential Clovibactin-like Antibiotics Binding to Unique Cell Wall Precursors in Diverse Gram-Positive Bacterial Strains

The rise in multidrug-resistant bacteria highlights the critical need for novel antibiotics. This study explores clovibactin-like compounds as potential therapeutic agents targeting lipid II, a crucial component in bacterial cell wall synthesis, using in silico techniques. A total of 2624 clovibactin analogs were sourced from the PubChem database and screened using ProTox 3.0 software based on their ADME-Tox properties, prioritizing candidates with favorable pharmacokinetic profiles and minimal toxicity. Molecular docking protocols were then employed to assess the binding interactions of the selected compounds with lipid II. Our analysis identified Compound 22 as a particularly promising candidate, exhibiting strong binding affinity, stable complex formation, and high selectivity for the target. Binding energy analysis, conducted via molecular dynamics simulations, revealed a highly negative value of -25.50 kcal/mol for Compound 22, surpassing that of clovibactin and underscoring its potential efficacy. In addition, Compound 22 was prioritized due to its exceptional binding affinity to lipid II and its favorable ADME-Tox properties, suggesting a lower likelihood of adverse effects. These characteristics position Compound 22 as a promising candidate for further pharmacological development. While our computational results are encouraging, experimental validation is essential to confirm the efficacy and safety of these compounds. This study not only advances our understanding of clovibactin analogs but also contributes to the ongoing efforts to combat antimicrobial resistance through innovative antibiotic development.

In silico analysis of the effect of HCV genotype-specific polymorphisms in Core, NS3, NS5A, and NS5B proteins on T-cell epitope processing and presentation

Background

HCV genotypes are 30-35% polymorphic at the nucleotide level, while subtypes within the same genotype differ by nearly 20%. Although previous studies have shown the immune escape potential of several mutations within the HCV proteins, little is known about the effect of genotype/subtype-specific gene polymorphism on T-cell immunity. Therefore, this study employed several in silico methods to examine the impact of genotype/subtype-specific polymorphisms in Core, NS3, NS5A, and NS5B sequences on T cell epitope processing and HLA-epitope interactions.

Methods

For this study, 8,942, 17,700, 14,645, and 3,277 HCV Core, NS3, NS5A, and NS5B sequences, respectively, from eight genotypes and 21 subtypes were retrieved from the Los Alamos HCV Database. Next, the NetCTL tool was employed to predict Cytotoxic T Lymphocyte (CTL) epitopes based on combined proteasomal cleavage, TAP efficacy, and HLA class I receptor binding scores. PEP-FOLD was used to model selected epitopes, followed by peptide-HLA docking using HPEPDOCK. Finally, molecular dynamics simulations were conducted for 200 ns using Desmond software to analyze differences in HLA-epitope (from different HCV genotypes) interaction kinetics and dynamics.

Results

A total of 3,410, 8,054, 6,532, and 14,015 CTL epitopes were observed in the HCV Core, NS3, NS5A, and NS5B sequences, respectively. Significant genotype/subtype-specific variations in CTL values and docking scores were observed among NS3, NS5A, and NS5B proteins. In silico results reveal that epitopes from genotype 6b (NS3), 6d/r (NS5B), 6o and 6 k (NS5A) exhibit higher immunogenicity than other genotypes, forming more energetically stable complexes with host receptors. These epitopes, compared to those from the same positions but different genotypes, showed binding energies of -144.24 kcal/mol, -85.30 kcal/mol, and - 43 kcal/mol, respectively. Over a 200 ns MD simulation, GT 6b and 6d/r epitopes displayed up to a 40% stronger binding energy with the HLA receptor. These findings suggest that patients infected with GT 6 may experience enhanced T cell responsiveness and broader immunogenicity.

Conclusion

Our study suggests that genotype/subtype-specific polymorphism in HCV may result in altered immune responses by modulating T-cell epitope processing and interaction with HLA receptors. Further experimental studies can be performed to confirm the effect of genotype/subtype-specific polymorphisms on T cell-mediated immune response.

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent

Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Cracking the code of adaptive immunity: The role of computational tools

In recent years, the advances in high-throughput and deep sequencing have generated a diverse amount of adaptive immune repertoire data. This surge in data has seen a proportional increase in computational methods aimed to characterize T cell receptor (TCR) repertoires. In this perspective, we will provide a brief commentary on the various domains of TCR repertoire analysis, their respective computational methods, and the ongoing challenges. Given the breadth of methods and applications of TCR analysis, we will focus our perspective on sequence-based computational methods.

Molecular dynamics-based computational investigations on the influence of tumor suppressor p53 binding protein against other proteins/peptides

The tumor-suppressing p-53 binding protein is a crucial protein that is involved in the prevention of cancer via its regulatory effect on a number of cellular processes. Recent evidence indicates that it interacts with a number of other proteins involved in cancer in ways that are not fully understood. An understanding of such interactions could provide insights into novel ways p53 further exerts its tumour prevention role via its interactions with diverse proteins. Thus, this study aimed to examine the interactions of the p53 protein with other proteins (peptides and histones) using molecular simulation dynamics. We opted for a total of seven proteins, namely 2LVM, 2MWO, 2MWP, 4CRI, 4 × 34, 5Z78, and 6MYO (control), and had their PBD files retrieved from the protein database. These proteins were then docked against the p-53 protein and the resulting interactions were examined using molecular docking simulations run at 500 ns. The result of the interactions revealed the utilisation of various amino acids in the process. The peptide that interacted with the highest number of amino acids was 5Z78 and these were Lys10, Gly21, Trp24, Pro105, His106, and Arg107, indicating a stronger interaction. The RMSD and RMSF values indicate that the complexes formed were stable, with 4CRI, 6MYO, and 2G3R giving the most stable values (less than 2.5 Å). Other parameters, including the SASA, Rg, and number of hydrogen bonds, all indicated the formation of fairly stable complexes. Our study indicates that overall, the interactions of 53BP1 with p53K370me2, p53K382me2, methylated K810 Rb, p53K381acK382me2, and tudor-interacting repair regulator protein indicated interactions that were not as strong as those with the histone protein. Thus, it could be that P53 may mediate its tumour suppressing effect via interactions with amino acids and histone.

Nuclear localization of Arabidopsis HD-Zip IV transcription factor GLABRA2 is driven by importin α

GLABRA2 (GL2), a class IV homeodomain leucine-zipper (HD-Zip IV) transcription factor from Arabidopsis, is a developmental regulator of specialized cell types in the epidermis. GL2 contains a monopartite nuclear localization sequence (NLS) that is conserved in most HD-Zip IV members across the plants. We demonstrate that NLS mutations affect nuclear transport and result in a loss-of-function phenotypes. NLS fusions to enhanced yellow fluorescent protein (EYFP) show that it is sufficient for nuclear localization in roots and trichomes. Despite partial overlap of the NLS with the homeodomain, genetic dissection indicates that nuclear localization and DNA binding are separable functions. Affinity purification of GL2 from plants followed by MS-based proteomics identified importin α (IMPα) isoforms as potential GL2 interactors. NLS structural prediction and molecular docking studies with IMPα-3 revealed major interacting residues. Cytosolic yeast two-hybrid assays and co-immunoprecipitation experiments with recombinant proteins verified NLS-dependent interactions between GL2 and several IMPα isoforms. IMPα triple mutants (impα-1,2,3) exhibit abnormal trichome formation and defects in GL2 nuclear localization in trichomes, consistent with tissue-specific and redundant functions of IMPα isoforms. Taken together, our findings provide mechanistic evidence for IMPα-dependent nuclear localization of GL2 in Arabidopsis, a process that is critical for cell type differentiation of the epidermis.

Mechanistic study of inhibitory peptides with SHP-1 in hypertonic environment for infection model

Cutaneous Leishmaniasis, an infectious disease is globally the most prevalent form of leishmaniasis accounting for approximately 1 million cases every year as per world health organization. Infected individuals develop skin lesion which has been reported to be infiltrated by immune cells and parasite with high sodium accumulation creating hypertonic environment. In our work, we tried to mimic the hypertonic environment in virtual environment to study dynamicity of SHP-1 and NFAT5 along with their interactions through molecular dynamics simulation. We validated the SHP-1 and NFAT5 dynamics in infection and HSD conditions to study the impact of hypertonicity derived NFAT5 mediated response to L.major infection. We also evaluated our therapeutic peptides for their binding to SHP-1 and to form stable complex. Membrane stability with the peptides was analyzed to understand their ability to sustain mammalian membrane. We identified PepA to be a potential candidate to interact with SHP-1. Inhibition of SHP-1 through PepA to discern IL-10 and IL-12 reciprocity may be assessed in future and furnish us with a potential therapeutic molecule. HSD mice exhibited high pro-inflammatory response to L.major infection which resulted in reduced lesion size. Contrary to observations in HSD mice, infection model exhibited low pro-inflammatory response and increased lesion size with high parasite load. Thus, increase in NFAT5 expression and reduced SHP-1 expression may result in disease resolving effect which can be further studied through incorporation of synthetic circuit using PepA to modulate IL-10 and IL-12 reciprocity.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Peptide Drug Design Using Alchemical Free Energy Calculation: An Application and Validation on Agonists of Ghrelin Receptor

With recent large-scale applications and validations, the relative binding free energy (RBFE) calculated using alchemical free energy methods has been proven to be an accurate measure to probe the binding of small-molecule drug candidates. On the other hand, given the flexibility of peptides, it is of great interest to find out whether sufficient sampling could be achieved within the typical time scale of such calculation, and a similar level of accuracy could be reached for peptide drugs. However, the systematic evaluation of such calculations on protein-peptide systems has been less reported. Most reported studies of peptides were restricted to a limited number of data points or lacking experimental support. To demonstrate the applicability of the alchemical free energy method for protein-peptide systems in a typical real-world drug discovery project, we report an application of the thermodynamic integration (TI) method to the RBFE calculation of ghrelin receptor and its peptide agonists. Along with the calculation, the synthesis and in vitro EC50 activity of relamorelin and 17 new peptide derivatives were also reported. A cost-effective criterion to determine the data collection time was proposed for peptides in the TI simulation. The average of three TI repeats yielded a mean absolute error of 0.98 kcal/mol and Pearson's correlation coefficient (R) of 0.77 against the experimental free energy derived from the in vitro EC50 activity, showing good repeatability of the proposed method and a slightly better agreement than the results obtained from the arbitrary time frames up to 20 ns. Although it is limited by having one target and a deduced binding pose, we hope that this study can add some insights into alchemical free energy calculation of protein-peptide systems, providing theoretical assistance to the development of peptide drugs.

Proteomic Characterization of Human Placenta: Insights into Potential Therapeutic Applications for Osteoarthritis

Biologics have become increasingly prominent as therapeutics in recent years due to their innate immune-privileged nature, biocompatibility, and high levels of protein biofactors. The aim of the study is to characterise the biologic, lyophilized human placenta (LHP) and explore its therapeutic potential for osteoarthritis (OA). The presence of six bioactive constituents that regulate cell-extracellular matrix interaction was identified by liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF/MS). Metalloproteinase inhibitor 3 (TIMP3), alpha-1 anti-trypsin (a1AT), basic fibroblast growth factor (bFGF), and transforming growth factor β1 (TGFβ1) were detected and quantified using ELISA. The total protein content present in LHP by Bradford assay was found to be 409.35 ± 0.005 μg/ml. The analytical techniques such as Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR), solid state carbon-13 Nuclear Magnetic Resonance (ssC13 NMR) spectroscopy, and Differential Scanning Calorimetry (DSC) revealed the secondary structure and conformational stability of LHP. X-Ray diffraction (XRD) studies showed its amorphous nature. Bioactivity assessment of LHP was performed in human keratinocytes (HaCaT) and human dermal fibroblasts (HDF) by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The LHP was highly proliferative against skin cells and non-toxic, based on the findings of the bioactivity assay. LHP has the potential to be used as a therapeutic agent for OA, as its characterisation unveiled its physical stability, significant concentration of bioactive components that are pertinent to cartilage repair and its conformational stability.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Deciphering ACE2-RBD binding affinity through peptide scanning: A molecular dynamics simulation approach

Rapid discovery of target information for protein-protein interactions (PPIs) is significant in drug design, diagnostics, vaccine development, antibody therapy, etc. Peptide microarray is an ideal tool for revealing epitope information of PPIs. In this work, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) and the host cell receptor angiotensin-converting enzyme 2 (ACE2) were introduced as a model to study the epitope information of RBD-specific binding to ACE2 via a combination of theoretical calculations and experimental validation. Through dock and molecular dynamics simulations, it was found that among the 22 peptide fragments that consist of RBD, #14 (YNYLYRLFRKSNLKP) has the highest binding strength. Subsequently, the experiments of peptide microarray constructed based on plasmonic materials chip also confirmed the theoretical calculation data. Compared to other methods, such as phage display technology and surface plasmon resonance (SPR), this method is rapid and cost-effective, providing insights into the investigation of pathogen invasion processes and the timely development of peptide drugs and other fields.

Investigating the Protein-Based Therapeutic Relationship between Honey Protein SHP-60 and Bevacizumab on Angiogenesis: Exploring the Synergistic Effect through In Vitro and In Silico Analysis

Honey is a natural product produced by honeybees, which has been used not only as food but also as a medicine by humans for thousands of years. In this study, 60 kDa protein was purified from Pakistani Sidr honey named as SHP-60 (Sidr Honey Protein-60), and its antioxidant potential and the effect of Bevacizumab with purified protein on in vitro angiogenesis using human umbilical vein endothelial cells (HUVEC) were investigated. We further validated the molecular protein-protein (SHP-60 with Bevacizumab) interactions through in silico analysis. It showed very promising antioxidant activity by reducing 2,2-diphenyl-1-picrylhydrazyl free radicals with a maximum of 83% inhibition at 50 μM and an IC50 of 26.45 μM statistically significant (**p < 0.01). Angiogenesis is considered a hallmark of cancer, and without it, the tumor cannot grow or metastasize. Bevacizumab, SHP-60, and both in combination were used to treat HUVEC, and the MTT assay was used to assess cell viability. To demonstrate in vitro angiogenesis, HUVEC was grown on Geltrex, and the formation of endotubes was examined using a tube formation assay. HUVEC viability was dose-dependently decreased by Bevacizumab, SHP-60, and both together. Bevacizumab and SHP-60 both inhibited angiogenesis in vitro, and their combination displayed levels of inhibition even higher than those of Bevacizumab alone. We investigated the protein-protein molecular docking interactions and molecular dynamics simulation analysis of MRJP3 (major royal jelly protein 3) similar to SHP-60 in molecular weight with both the heavy chain (HC) and light chain (LC) of Bevacizumab. We found significant interactions between the LC and MRJP3, indicating that ASN468, GLN470, and ASN473 of MRJP3 interact with SER156, SER159, and GLU161 of LC of Bevacizumab. The integration of experimental data and computational techniques is believed to improve the reliability of the findings and aid in future drug design.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study

OBJECTIVES

Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.

METHODS

Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.

RESULTS

Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.

CONCLUSION

This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

Molecular Dynamics Simulations in Protein-Protein Docking

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Nuclear localization of HD-Zip IV transcription factor GLABRA2 is driven by Importin α

GLABRA2 (GL2), a class IV homeodomain leucine-zipper (HD-Zip IV) transcription factor (TF) from Arabidopsis , is a developmental regulator of specialized cell types in the epidermis. GL2 contains a putative monopartite nuclear localization sequence (NLS) partially overlapping with its homeodomain (HD). We demonstrate that NLS deletion or alanine substitution of its basic residues (KRKRKK) affects nuclear localization and results in a loss-of-function phenotype. Fusion of the predicted NLS (GTNKRKRKKYHRH) to the fluorescent protein EYFP is sufficient for its nuclear localization in roots and trichomes. The functional NLS is evolutionarily conserved in a distinct subset of HD-Zip IV members including PROTODERMAL FACTOR2 (PDF2). Despite partial overlap of the NLS with the HD, genetic dissection of the NLS from PDF2 indicates that nuclear localization and DNA binding are separable functions. Affinity purification of GL2 from plant tissues followed by mass spectrometry-based proteomics identified Importin α (IMPα) isoforms as potential GL2 interactors. NLS structural prediction and molecular docking studies with IMPα-3 revealed major interacting residues. Split-ubiquitin cytosolic yeast two-hybrid assays suggest interaction between GL2 and four IMPα isoforms from Arabidopsis. Direct interactions were verified in vitro by co-immunoprecipitation with recombinant proteins. IMPα triple mutants ( impα- 1,2,3 ) exhibit defects in EYFP:GL2 nuclear localization in trichomes but not in roots, consistent with tissue-specific and redundant functions of IMPα isoforms in Arabidopsis . Taken together, our findings provide mechanistic evidence for IMPα-dependent nuclear localization of GL2 and other HD-Zip IV TFs in plants.

One sentence summary

GLABRA2, a representative HD-Zip IV transcription factor from Arabidopsis , contains an evolutionarily conserved monopartite nuclear localization sequence that is recognized by Importin α for translocation to the nucleus, a process that is necessary for cell-type differentiation of the epidermis.

Application of Molecular Dynamics Simulations to Determine Interactions between Canary Seed (Phalaris canariensis L.) Bioactive Peptides and Skin-Aging Enzymes

Food bioactive peptides are well recognized for their health benefits such as antimicrobial, antioxidant, and antihypertensive benefits, among others. Their drug-like behavior has led to their potential use in targeting skin-related aging factors like the inhibition of enzymes related with the skin-aging process. In this study, canary seed peptides (CSP) after simulated gastrointestinal digestion (<3 kDa) were fractioned by RP-HPLC and their enzyme-inhibition activity towards elastase and tyrosinase was evaluated in vitro. CSP inhibited elastase (IC50 = 6.2 mg/mL) and tyrosinase (IC50 = 6.1 mg/mL), while the hydrophobic fraction-VI (0.2 mg/mL) showed the highest inhibition towards elastase (93%) and tyrosinase (67%). The peptide fraction with the highest inhibition was further characterized by a multilevel in silico workflow, including physicochemical descriptor calculations, antioxidant activity predictions, and molecular dynamics-ensemble docking towards elastase and tyrosinase. To gain insights into the skin permeation process during molecular dynamics simulations, based on their docking scores, five peptides (GGWH, VPPH, EGLEPNHRVE, FLPH, and RPVNKYTPPQ) were identified to have favorable intermolecular interactions, such as hydrogen bonding of polar residues (W, H, and K) to lipid polar groups and 2-3 Å van der Waals close contact of hydrophobic aliphatic residues (P, V, and L). These interactions can play a critical role for the passive insertion of peptides into stratum corneum model skin-membranes, suggesting a promising application of CSP for skin-aging treatments.

Molecular Insights into the Improved Bioactivity of Interferon Conjugates Attached to a Helical Polyglutamate

Attaching polymers, especially polyethylene glycol (PEG), to protein drugs has emerged as a successful strategy to prolong circulation time in the bloodstream. The hypothesis is that the flexible chain wobbles on the protein's surface, thus resisting potential nonspecific adsorption. Such a theoretical framework may be challenged when a helical polyglutamate is used to conjugate with target proteins. In this study, we investigated the structure-activity relationships of polyglutamate-interferon conjugates P(EG₃Glu)-IFN using molecular simulations. Our results show that the local crowding effect induced by oligoethylene glycols (i.e., EG₃) is the primary driving force for helix formation in P(EG₃Glu), and its helicity can be effectively increased by reducing the free volume of the two termini. Furthermore, it was found that the steric hindrance induced by IFN is not conductive to the helicity of P(EG₃Glu) but contributes to its dominant orientation relative to interferon. The orientation of IFN relative to the helical P(EG₃Glu) can help to protect the protein drug from neutralizing antibodies while maintaining its bioactivity. These findings suggest that the helical structure and its orientation are critical factors to consider when updating the theoretical framework for protein-polymer conjugates.

Defective ORF8 dimerization in SARS-CoV-2 delta variant leads to a better adaptive immune response due to abrogation of ORF8-MHC1 interaction

In India, during the second wave of the COVID-19 pandemic, the breakthrough infections were mainly caused by the SARS-COV-2 delta variant (B.1.617.2). It was reported that, among majority of the infections due to the delta variant, only 9.8% percent cases required hospitalization, whereas only 0.4% fatality was observed. Sudden dropdown in COVID-19 infections cases were observed within a short timeframe, suggesting better host adaptation with evolved delta variant. Downregulation of host immune response against SARS-CoV-2 by ORF8 induced MHC-I degradation has been reported earlier. The Delta variant carried mutations (deletion) at Asp119 and Phe120 amino acids which are critical for ORF8 dimerization. The deletions of amino acids Asp119 and Phe120 in ORF8 of delta variant resulted in structural instability of ORF8 dimer caused by disruption of hydrogen bonds and salt bridges as revealed by structural analysis and MD simulation studies. Further, flexible docking of wild type and mutant ORF8 dimer revealed reduced interaction of mutant ORF8 dimer with MHC-I as compared to wild-type ORF8 dimer with MHC-1, thus implicating its possible role in MHC-I expression and host immune response against SARS-CoV-2. We thus propose that mutant ORF8 of SARS-CoV-2 delta variant may not be hindering the MHC-I expression thereby resulting in a better immune response against the SARS-CoV-2 delta variant, which partly explains the possible reason for sudden drop of SARS-CoV-2 infection rate in the second wave of SARS-CoV-2 predominated by delta variant in India.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

Molecular Characterization, Purification, and Mode of Action of Enterocin KAE01 from Lactic Acid Bacteria and Its In Silico Analysis against MDR/ESBL Pseudomonas aeruginosa

Bacteriocins are gaining immense importance in therapeutics since they show significant antibacterial potential. This study reports the bacteriocin KAE01 from Enterococcus faecium, along with its characterization, molecular modeling, and antibacterial potency, by targeting the matrix protein of Pseudomonas aeruginosa. The bacteriocin was purified by using ammonium sulfate precipitation and fast protein liquid chromatography (FPLC), and its molecular weight was estimated as 55 kDa by means of SDS-PAGE. The bacteriocin was found to show stability in a wide range of pH values (2.0-10.0) and temperatures (100 °C for 1 h and 121 °C for 15 min). Antimicrobial screening of the purified peptide against different strains of P. aeruginosa showed its significant antibacterial potential. Scanning electron microscopy of bacteriocin-induced bacterial cultures revealed significant changes in the cellular morphology of the pathogens. In silico molecular modeling of KAE01, followed by molecular docking of the matrix protein (qSA) of P. aeruginosa and KAE01, supported the antibacterial potency and SEM findings of this study.

Absolute binding free energies of mucroporin and its analog mucroporin-M1 with the heptad repeat 1 domain and RNA-dependent RNA polymerase of SARS-CoV-2

The peptide Mucroporin and its analog Mucroporin-M1 were studied using the molecular docking and molecular dynamics simulation of their complexation with two protein targets, the Heptad Repeat 1 (HR1) domain and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. The molecular docking of the peptide-protein complexes was performed using the glowworm swarm optimization algorithm. The lowest energy poses were submitted to molecular dynamics simulation. Then, the binding free energies of Mucroporin and its analog Mucroporin-M1 with these two protein targets were calculated using the Multistate Bennett Acceptance Ratio (MBAR) method. It was verified that the peptides/HR1 domain complex showed stability in the interaction site determined by molecular docking. It was also found that Mucroporin-M1 has a much higher affinity than Mucroporin to the HR1 protein target. The peptides showed similar stability and affinity at the NTP binding site in the RdRp protein. Additional experimental studies are needed to confirm the antiviral activity of Mucroporin-M1 and a possible mechanism of action against SARS-CoV-2. However, here we indicate that Mucroporin-M1 may have potential antiviral activity against the HR1 domain with the possibility for further peptide optimization.Communicated by Ramaswamy H. Sarma.

A plausible contributor to multiple sclerosis; presentation of antigenic myelin protein epitopes by major histocompatibility complexes

BACKGROUND

Multiple sclerosis (MS) can be induced upon successful presentation of myelin antigens by MHC I/II. Antigenic similarity between the myelin and viral proteins may worsen the immunological responses.

METHODOLOGY

Antigenic regions within myelin proteins; PLP1, MBP, MOG, and MAG were analyzed using SVMTrip and EMBOSS. Homology search identified sequence similarity between the predicted host epitopes and viral proteins. NetMHCpan predicted MHC I/II binding followed by peptide-protein docking through the HPEPDOCK server. Thereafter we analyzed conformational flexibility and stability of 15 protein-peptide complexes based on high docking scores. The binding free energy was calculated using conventional (MD) and Gaussian accelerated molecular dynamics simulation.

RESULTS

PLP1, MBP, MAG and MOG contained numerous antigenic epitopes. MBP and MOG epitopes had sequence similarity to HHV-6 BALF5; EBNA1 and CMV glycoprotein M (gM), and EBV LMP2B, gp350/220; HHV-8 ORFs respectively. Many herpes virus proteins like tegument, envelope glycoproteins, and ORFs of EBV, CMV, HHV-6, and HHV-8 demonstrated sequence similarity with MAG and PLP1. Some antigenic peptides were also linear B-cell epitopes and influenced cytokine production by T-cell. MHC I allele HLA-B*57:01 bound to PLP1 peptide and HLA-A*68:02 bound to a MAG peptide strongly. MHC II alleles HLA-DRB1*04:05 and HLA-DR1*01:01 associated with MAG- and MOG-derived peptides, respectively, demonstrating high HPEPDOCK scores. MD simulations established stable binding of certain peptides with the MHC namely HLA-B*51:01-MBP(DYKSAHKGFKGVDAQGTLSKIFKL), HLA-B*57:01-PLP1(PDKFVGITYALTVVWLLVFACSAVPVYIYF), HLA-DR1*01:01-MOG(VEDPFYWVSPGVLVLLAVLPVLLLQITVGLVFLCLQYR) and HLA-DRB1*04:05-MAG(TWVQVSLLHFVPTREA).

CONCLUSIONS

Cross-reactivity between self-antigens and pathogen derived immunodominant epitopes may induce MS. Our study supported the role of specific MHC alleles as a contributing MS risk factor.

Inhibition constant and stability of tripeptide inhibitors of gut trypsin-like enzyme of the soybean pest Anticarsia gemmatalis

Insects overcome the action of natural protease inhibitors (PIs) due to evolutionary adaptations through endogenous proteolysis and reprogramming proteases. Insect adaptations complicate the formulation of IP-based crop protection products. However, small peptides designed based on the active site of enzymes have shown promising results that could change this scenario. GORE1 and GORE2 are designed tripeptides that reduce the survival of Anticarsia gemmatalis when ingested orally. In this article, the stability and ability of the peptides to bind trypsin-like enzymes of A. gemmatalis were evaluated by molecular dynamics (MD) simulations. The ability of the peptides to inhibit trypsin-like enzymes in vivo was compared with the SKTI protein by feeding A. gemmatalis larvae at different concentrations, followed by an inhibition persistence assay. During the MD simulation of enzyme-ligand complexes, both peptides showed a small variation of root-mean-square deviation and root-mean-square fluctuation, suggesting that these molecules reach equilibrium when forming a complex with the trypsin-like enzyme. Furthermore, both peptides form hydrogen bonds with substrate recognition sites of A. gemmatalis trypsin-like enzyme, with GORE2 having more interactions than GORE1. Larvae of A. gemmatalis exposed to the peptides and SKTI showed a similar reduction in proteolytic activity, but the persistence of inhibition of trypsin-like enzyme was longer in peptide-fed insects. Despite their size, the peptides exhibit important active and substrate binding site interactions, stability during complex formation, and steadiness effects in vivo. The results provide fundamental information for the development of mimetic molecules and help in decision-making for the selection of delivery methods for larger-scale experiments regarding similar molecules.

Assessment of mutations on RBD in the Spike protein of SARS-CoV-2 Alpha, Delta and Omicron variants

The severe acute respiratory syndrome (SARS) coronavirus 2 (CoV-2) variant Omicron spread more rapid than the other variants of SARS-CoV-2 virus. Mutations on the Spike (S) protein receptor-binding domain (RBD) are critical for the antibody resistance and infectivity of the SARS-CoV-2 variants. In this study, we have used accelerated molecular dynamics (aMD) simulations and free energy calculations to present a systematic analysis of the affinity and conformational dynamics along with the interactions that drive the binding between Spike protein RBD and human angiotensin-converting enzyme 2 (ACE2) receptor. We evaluate the impacts of the key mutation that occur in the RBDs Omicron and other variants in the binding with the human ACE2 receptor. The results show that S protein Omicron has stronger binding to the ACE2 than other variants. The evaluation of the decomposition energy per residue shows the mutations N440K, T478K, Q493R and Q498R observed in Spike protein of SARS-CoV-2 provided a stabilization effect for the interaction between the SARS-CoV-2 RBD and ACE2. Overall, the results demonstrate that faster spreading of SARS-CoV-2 Omicron may be correlated with binding affinity of S protein RBD to ACE2 and mutations of uncharged residues to positively charged residues such as Lys and Arg in key positions in the RBD.

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.

Theoretical exploration of the binding selectivity of inhibitors to BRD7 and BRD9 with multiple short molecular dynamics simulations

Bromodomain-containing proteins 7 and 9 (BRD7 and BRD9) have been considered as potential targets of clinical drug design toward treatment of human cancers and other diseases. Multiple short molecular dynamics simulations and binding free energy predictions were carried out to decipher the binding selectivity of three inhibitors 4L2, 5U6, and 6KT toward BRD7 and BRD9. The results show that 4L2 has more favorable binding ability to BRD7 over BRD9 compared to 5U6 and 6KT, while 5U6 and 6KT possess more favorable associations with BRD9 than BRD7. Furthermore, estimations of residue-based free energy decompositions further identify that four common residue pairs, including (F155, F44), (V160, V49), (Y168, Y57) and (Y217, Y106) in (BRD7, BRD9) generate obvious binding differences with 4L2, 5U6, and 6KT, which mostly drives the binding selectivity of 4L2, 5U6, and 6KT to BRD7 and BRD9. Dynamic information arising from trajectory analysis also suggests that inhibitor bindings affect structural flexibility and motion modes, which is responsible for the partial selectivity of 4L2, 5U6, and 6KT toward BRD7 and BRD9. As per our expectation, this study theoretically provides useful hints for design of dual inhibitors with high selectivity on BRD7 and BRD9.

Bromodomains (BRDs) are structurally conserved epigenetic reader modules observed in numerous chromatin- and transcription-associated proteins that have a capability to identify acetylated lysine residues.

Unravelling the molecular interactions between the SARS-CoV-2 RBD spike protein and various specific monoclonal antibodies

Vaccination against SARS-CoV-2 just started in most of the countries. However, the development of specific vaccines against SARS-CoV-2 is not the only approach to control the virus and monoclonal antibodies (mAbs) start to merit special attention as a therapeutic option to treat COVID-19 disease. Here, the main conformations and interactions between the receptor-binding domain (RBD) of spike glycoprotein of SARS-CoV-2 (S protein) with two mAbs (CR3022 and S309) and the ACE2 cell receptor are studied as the main representatives of three different epitopes on the RBD of S protein. The combined approach of 1 μs accelerated molecular dynamics (aMD) and ab-initio hybrid molecular dynamics is used to identify the most predominant interactions under physiological conditions. Results allow to determine the main receptor-binding mapping, hydrogen bonding network and salt bridges in the most populated antigen-antibody interface conformations. The deep knowledge on the protein-protein interactions involving mAbs and ACE2 receptor with the spike glycoprotein of SARS-CoV-2 increases background knowledge to speed up the development of new vaccines and therapeutic drugs.

Tailored Modeling of Rivastigmine Derivatives as Dual Acetylcholinesterase and Butyrylcholinesterase Inhibitors for Alzheimer's Disease Treatment

Rational modification of known drug candidates to design more potent ones using computational methods has found application in drug design, development, and discovery. Herein, we integrate computational and theoretical methodologies to unveil rivastigmine derivatives as dual inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) for Alzheimer's disease (AD) management. The investigation entails pharmacokinetics screening, density functional theory (DFT) mechanistic study, molecular docking, and molecular dynamics (MD) simulation. We designed over 20 rivastigmine substituents, subject them to some analyses, and identified RL2 with an appreciable blood-brain barrier score and no permeability glycoprotein binding. The compound shows higher acylation energy and a favored binding affinity to the cholinesterase enzymes. RL2 interacts with the AChE and BuChE active sites showing values of -41.1/-39.5 kcal mol^-1 while rivastigmine binds with -32.7/-30.7 kcal mol^-1 for these enzymes. The study revealed RL2 (4-fluorophenyl rivastigmine) as a potential dual inhibitor for AChE and BuChE towards Alzheimer's disorder management.

Native or Non-Native Protein-Protein Docking Models? Molecular Dynamics to the Rescue

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favorable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow distinguishing native from non-native models to complement scoring functions used in docking. To this end, the first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the Critical Assessment of PRedicted Interaction (CAPRI) competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A random forest classifier was trained, reaching a 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths of the order of 50-100 ns are sufficient to reach this accuracy, which makes this approach applicable in practice.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Binding of Inhibitors to BACE1 Affected by pH-Dependent Protonation: An Exploration from Multiple Replica Gaussian Accelerated Molecular Dynamics and MM-GBSA Calculations

To date, inhibiting the activity of β-amyloid cleaving enzyme 1 (BACE1) has been considered an efficient approach for treating Alzheimer's disease (AD). In the current work, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics general Born surface area (MM-GBSA) method were combined to investigate the effect of pH-dependent protonation on the binding of the inhibitors CS9, C6U, and 6WE to BACE1. Dynamic analyses based on the MR-GaMD trajectory show that pH-dependent protonation strongly affects the structural flexibility, correlated motions, and dynamic behavior of inhibitor-bound BACE1. According to the constructed free energy profiles, in the protonated state at low pH, inhibitor-bound BACE1 tends to populate at more conformations than in high pH. The binding free energies calculated by MM-GBSA suggest that inhibitors possess stronger binding abilities under the protonation conditions at high pH than under the protonation conditions at low pH. Moreover, pH-dependent protonation exerts a significant effect on the hydrogen bonding interactions of CS9, C6U, and 6WE to BACE1, which correspondingly alters the binding abilities of the three inhibitors to BACE1. Furthermore, in different protonated environments, three inhibitors share common interaction clusters and similar binding sites in BACE1, which are reliably used as efficient targets for the design of potent inhibitors of BACE1.

Antiviral effects of probiotic metabolites on COVID-19

SARS coronavirus (COVID-19) is a real health challenge of the 21st century for scientists, health workers, politicians, and all humans that has severe cause epidemic worldwide. The virus exerts its pathogenic activity through by mechanism and gains the entry via spike proteins (S) and Angiotensin-Converting Enzyme 2 (ACE2) receptor proteins on host cells. The present work is an effort for a computational target to block the residual binding protein (RBP) on spike proteins (S), Angiotensin-Converting Enzyme 2 (ACE2) receptor proteins by probiotics namely Plantaricin BN, Plantaricin JLA-9, Plantaricin W, Plantaricin D along with RNA-dependent RNA polymerase (RdRp). Docking studies were designed in order to obtain the binding energies for Plantaricin metabolites. The binding energies for Plantaricin W were -14.64, -11.1 and -12.68 for polymerase, RBD and ACE2 respectively comparatively very high with other compounds. Plantaricin W, D, and JLA-9 were able to block the residues (THR556, ALA558) surrounding the deep grove catalytic site (VAL557) of RdRp making them more therapeutically active for COVID-19. Molecular dynamics studies further strengthen stability of the complexes of plantaricin w and SARS-CoV-2 RdRp enzyme, RBD of spike protein, and human ACE2 receptor. The present study present multi-way options either by blocking RBD on S proteins or interaction of S protein with ACE2 receptor proteins or inhibiting RdRp to counter any effect of COVID-19 by Plantaricin molecules paving a way that can be useful in the treatment of COVID-19 until some better option will be available.Communicated by Ramaswamy H. Sarma.

Targeting SARS-CoV-2 Receptor Binding Domain with Stapled Peptides: An In Silico Study

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into a pandemic of unprecedented scale. This coronavirus enters cells by the interaction of the receptor binding domain (RBD) with the human angiotensin-converting enzyme 2 receptor (hACE2). In this study, we employed a rational structure-based design to propose 22-mer stapled peptides using the structure of the hACE2 α1 helix as a template. These peptides were designed to retain the α-helical character of the natural structure, to enhance binding affinity, and to display a better solubility profile compared to other designed peptides available in the literature. We employed different docking strategies (PATCHDOCK and ZDOCK) followed by a double-step refinement process (FIBERDOCK) to rank our peptides, followed by stability analysis/evaluation of the interaction profile of the best docking predictions using a 500 ns molecular dynamics (MD) simulation, and a further binding affinity analysis by molecular mechanics with generalized Born and surface area (MM/GBSA) method. Our most promising stapled peptides presented a stable profile and could retain important interactions with the RBD in the presence of the E484K RBD mutation. We predict that these peptides can bind to the viral RBD with similar potency to the control NYBSP-4 (a 30-mer experimentally proven peptide inhibitor). Furthermore, our study provides valuable information for the rational design of double-stapled peptide as inhibitors of SARS-CoV-2 infection.

Simulation of the Positive Inotropic Peptide S100A1ct in Aqueous Environment by Gaussian Accelerated Molecular Dynamics

The S100A1ct peptide, consisting of the C-terminal 20 residues of the S100A1 protein fused to an N-terminal 6-residue hydrophilic tag, has been found to exert a positive inotropic effect, resulting in improved contractile performance of failing cardiac and skeletal muscle without arrhythmic side-effects. The S100A1ct peptide thus has high potential for the treatment of acute heart failure. As a step toward understanding its molecular mechanism of action, and to provide a basis for peptidomimetic design to optimize its properties, we here describe de novo structure predictions and molecular dynamics simulations to characterize the conformational landscape of S100A1ct in aqueous environment. In S100A1, the C-terminal 20 residues form an α-helix, but de novo peptide structure predictions indicate that other conformations are also possible. Conventional molecular dynamics simulations in implicit and explicit solvent corroborated this finding. To ensure adequate sampling, we performed simulations of a tagged 10-residue segment of S100A1ct, and we carried out Gaussian accelerated molecular dynamics simulations of the peptides. These simulations showed that although the helical conformation of S100A1ct was the most energetically stable, the peptide can adopt a range of kinked conformations, suggesting that its activity may be related to its ability to act as a conformational switch.

Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.

Rational Design of Novel Glycomimetic Peptides for E-Selectin Targeting

E-selectin is a cell-adhesion receptor with specific recognition capacity toward sialo-fucosylated Lewis carbohydrates present in leukocytes and tumor cells. E-selectin interactions mediate the progress of inflammatory processes and tumor metastasis, which aroused the interest in using this protein as a biomolecular target to design glycomimetic inhibitors for active targeting or therapeutic purposes. In this work, we report the rational discovery of two novel glycomimetic peptides targeting E-selectin based on mutations of the reference selectin-binding peptide IELLQAR. Sixteen single or double mutants at Ile1, Leu3, Leu4, and Arg7 residues were evaluated as potential candidates for E-selectin targeting using 50 ns molecular dynamics (MD) simulations. Nine peptides showing a stable association with the functional pocket were modified by adding a cysteine residue to the N-terminus to confer versatility for further chemical conjugation. Subsequent 50 ns MD simulations resulted in five cysteine-modified peptides with retained or improved E-selectin binding potential. Then, 300 ns accelerated MD (aMD) simulations were used to examine the binding properties of the best five cysteine-modified peptides. CIEELQAR and CIELFQAR exhibit the most selective association with the functional pocket of E-selectin, as revealed by potential of mean force profiles. Microscale thermophoresis experiments confirmed the E-selectin binding capacity of the selected peptides with K_D values in the low micromolar range (CIEELQAR K_D = 35.0 ± 1.4 μM; CIELFQAR K_D = 16.4 ± 0.7 μM), which are 25-fold lower than the reported value for the native ligand sLe^x (K_D = 878 μM). Our findings support the potential of CIEELQAR and CIELFQAR as novel E-selectin-targeting peptides with high recognition capacity and versatility for chemical conjugation, which are critical for enabling future applications in active targeting.

An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor

The binding free energy calculation of protein-ligand complexes is necessary for research into virus-host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras-Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: -14.7(ΔGbindBondi)<-10.6(ΔGbindExp.)<-4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.

Mutation-Induced Impacts on the Switch Transformations of the GDP- and GTP-Bound K-Ras: Insights from Multiple Replica Gaussian Accelerated Molecular Dynamics and Free Energy Analysis

Mutations yield significant effect on the structural flexibility of two switch domains, SW1 and SW2, in K-Ras, which is considered as an important target of anticancer drug design. To unveil a molecular mechanism with regard to mutation-mediated tuning on the activity of K-Ras, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations followed by analysis of free energy landscapes (FELs) are performed on the GDP- and GTP-bound wild-type (WT), G12V, and D33E K-Ras. The results suggest that G12V and D33E not only evidently change the flexibility of SW1 and SW2 but also greatly affect correlated motions of SW1 and SW2 separately relative to the P-loop and SW1, which exerts a certain tuning on the activity of K-Ras. The information stemming from the analyses of FELs reveals that the conformations of SW1 and SW2 are in high disorders in the GDP- and GTP-associated WT and mutated K-Ras, possibly producing significant effect on binding of guanine nucleotide exchange factors or effectors to K-Ras. The interaction networks of GDP and GTP with K-Ras are identified and the results uncover that the instability in hydrogen-bonding interactions of SW1 with GDP and GTP is mostly responsible for conformational disorder of SW1 and SW2 as well as tunes the activity of oncogenic K-Ras.

Unveiling conformational dynamics changes of H-Ras induced by mutations based on accelerated molecular dynamics

Uncovering molecular basis with regard to the conformational change of two switches I and II in the GppNHp (GNP)-bound H-Ras is highly significant for the understanding of Ras signaling. For this purpose, accelerated molecular dynamics (aMD) simulations and principal component (PC) analysis are integrated to probe the effect of mutations G12V, T35S and Q61K on conformational transformation between two switches of the GNP-bound H-Ras. The RMSF and cross-correlation analyses suggest that three mutations exert a vital effect on the flexibility and internal dynamics of two switches in the GNP-bound H-Ras. The results stemming from PC analysis indicate that two switches in the GNP-bound WT H-Ras tend to form a closed state in most conformations, while those in the GNP-bound mutated H-Ras display transformation between different states. This conclusion is further supported by free energy landscapes constructed by using the distances of residues 12 away from 35 and 35 away from 61 as reaction coordinates and different experimental studies. Interaction scanning is performed on aMD trajectories and the information shows that conformational transformations of two switches I and II induced by mutations extremely affect the GNP-residue interactions. Meanwhile, the scanning results also signify that residues G15, A18, F28, K117, A146 and K147 form stable contacts with GNP, while residues D30, E31, Y32, D33, P34 and E62 in two switches I and II produce unstable contacts with GNP. This study not only reveals dynamic behavior changes of two switches in H-Ras induced by mutations, but also unveils general principles and mechanisms with regard to functional conformational changes of H-Ras.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.