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

Computational analysis of antimicrobial peptides targeting key receptors in infection-related cardiovascular diseases: molecular docking and dynamics insights

Infection-related cardiovascular diseases (CVDs) pose a significant health challenge, driving the need for novel therapeutic strategies to target key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) show the potential to disrupt pathogenic processes and offer a promising approach to CVD treatment. This study investigates the binding potential of selected AMPs with critical receptors implicated in CVDs, aiming to explore their therapeutic potential. A comprehensive computational approach was employed to assess AMP interactions with CVD-related receptors, including ACE2, CRP, MMP9, NLRP3, and TLR4. Molecular docking studies identified AMPs with high binding affinities to these targets, notably Tachystatin, Pleurocidin, and Subtilisin A, which showed strong interactions with ACE2, CRP, and MMP9. Following docking, 100 ns molecular dynamics (MD) simulations confirmed the stability of AMP-receptor complexes, and MM/PBSA calculations provided quantitative insights into binding energies, underscoring the potential of these AMPs to modulate receptor activity in infection and inflammation contexts. The study highlights the therapeutic potential of Tachystatin, Pleurocidin, and Subtilisin A in targeting infection-related pathways in CVDs. These AMPs demonstrate promising receptor binding properties and stability in computational models. Future research should focus on in vitro and in vivo studies to confirm their efficacy and safety, paving the way for potential clinical applications in managing infection-related cardiovascular conditions.

Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide-receptor complexes, along with Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin's interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides.

On delivering polar solvation free energy of proteins from energy minimized structures using a regularized super-Gaussian Poisson-Boltzmann model

The biomolecules interact with their partners in an aqueous media; thus, their solvation energy is an important thermodynamics quantity. In previous works (J. Chem. Theory Comput. 14(2): 1020-1032), we demonstrated that the Poisson-Boltzmann (PB) approach reproduces solvation energy calculated via thermodynamic integration (TI) protocol if the structures of proteins are kept rigid. However, proteins are not rigid bodies and computing their solvation energy must account for their flexibility. Typically, in the framework of PB calculations, this is done by collecting snapshots from molecular dynamics (MD) simulations, computing their solvation energies, and averaging to obtain the ensemble-averaged solvation energy, which is computationally demanding. To reduce the computational cost, we have proposed Gaussian/super-Gaussian-based methods for the dielectric function that use the atomic packing to deliver smooth dielectric function for the entire computational space, the protein and water phase, which allows the ensemble-averaged solvation energy to be computed from a single structure. One of the technical difficulties associated with the smooth dielectric function presentation with respect to polar solvation energy is the absence of a dielectric border between the protein and water where induced charges should be positioned. This motivated the present work, where we report a super-Gaussian regularized Poisson-Boltzmann method and use it for computing the polar solvation energy from single energy minimized structures and assess its ability to reproduce the ensemble-averaged polar solvation on a dataset of 74 high-resolution monomeric proteins.

Unlocking the wound-healing potential: An integrative in silico proteomics and in vivo analysis of Tacorin, a bioactive protein fraction from Ananas comosus (L.) Merr. Stem

Tacorin, a bioactive protein fraction derived from pineapple stem (Ananas comosus), has emerged as a promising therapeutic agent for wound healing. This study employs an integrated approach, combining in silico proteomics and in vivo investigations, to unravel the molecular mechanisms underlying Tacorin's wound healing properties. In the domain of in silico proteomics, the composition of Tacorin is elucidated through LC/MS-MS protein sequencing, revealing ananain (23.77 kDa) and Jacalin-like lectin (14.99 kDa) as its predominant constituents. Molecular protein-protein docking simulations unveil favorable interactions between Tacorin's components and key regulators of wound healing, including TGF-β, TNF-α, and MMP-2. The calculated free binding energies indicate strong binding affinities between Tacorin proteins and their target receptors. Specifically, ananain demonstrates a binding affinity of -12.2 kcal/mol with TGF-β, suggesting its potential as a potent activator of TGF-β-mediated signaling, while Jacalin-like lectin exhibits the most favorable binding affinity of -8.7 kcal/mol with TNF-α. Subsequent 100 ns molecular dynamics (MD) simulations provide insights into the dynamic behavior and stability of Tacorin-receptor complexes, shedding light on the molecular determinants of Tacorin's therapeutic effects. Complementing the in silico analyses, in vivo studies evaluate Tacorin's efficacy in wound healing using skin and uterine incision models. Tacorin treatment accelerates wound closure and promotes tissue repair in both models, as evidenced by macroscopic observations and histological assessments. Overall, this study provides compelling evidence of Tacorin's therapeutic potential in wound healing and underscores the importance of elucidating its molecular mechanisms for further development and clinical translation.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Unraveling modulation effects on albumin synthesis and inflammation by Striatin, a bioactive protein fraction isolated from Channa striata: In silico proteomics and in vitro approaches

Hypoalbuminemia, associated with inflammation in severely ill patients, can emerge due to decreased albumin production. Transforming growth factor-beta (TGF-β) and nuclear factor-kappa B (NF-κB) are critical signaling pathways responsible for decreased albumin expression. This study explores the protein content and modulation effects of Striatin on albumin synthesis and inflammation, employing in silico proteomics and in vitro investigations. In the in silico proteomics realm, LC/MS-MS protein sequencing, 3D modeling, protein-protein docking simulations, 100 ns molecular dynamics (MD) simulations, and MM/PBSA binding free energy calculations were carried out. Complementing this, in vitro studies examined Albumin gene expression and extracellular secretion in HepG2 cells subjected to lipopolysaccharides-induced hypoalbuminemia. Furthermore, the study probed Striatin's influence on the NF-ᴋB expression, given albumin's role as a negative acute-phase protein. The results unveiled nucleoside diphosphate kinase (NdK) and parvalbumin (PV) as the prominent constituents within Striatin. Notably, NdK and PV exhibited the ability to disrupt hydrogen bonds with specific residues in both TGF-β and NF-κB complexes, thereby enhancing their flexibility, akin to the action of the FKBP12 complex (antagonist complex). In the in vitro experiments, Striatin demonstrated a dose and time-dependent inhibition of hypoalbuminemia, with peak efficacy observed at a concentration of 20 μg/mL. At this concentration, Striatin also suppressed NF-κB expression when co-incubated with lipopolysaccharides. While these findings suggest potential inhibitory effects of Striatin on TGF-β and NF-κB activities, they are preliminary and warrant further investigation. This study highlights Striatin's potential as a therapeutic agent for inflammation-related hypoalbuminemia, though additional research is needed to fully validate these results.

Leveraging Therapeutic Proteins and Peptides from Lumbricus Earthworms: Targeting SOCS2 E3 Ligase for Cardiovascular Therapy through Molecular Dynamics Simulations

Suppressor of cytokine signaling 2 (SOCS2), an E3 ubiquitin ligase, regulates the JAK/STAT signaling pathway, essential for cytokine signaling and immune responses. Its dysregulation contributes to cardiovascular diseases (CVDs) by promoting abnormal cell growth, inflammation, and resistance to cell death. This study aimed to elucidate the molecular mechanisms underlying the interactions between Lumbricus-derived proteins and peptides and SOCS2, with a focus on identifying potential therapeutic candidates for CVDs. Utilizing a multifaceted approach, advanced computational methodologies, including 3D structure modeling, protein-protein docking, 100 ns molecular dynamics (MD) simulations, and MM/PBSA calculations, were employed to assess the binding affinities and functional implications of Lumbricus-derived proteins on SOCS2 activity. The findings revealed that certain proteins, such as Lumbricin, Chemoattractive glycoprotein ES20, and Lumbrokinase-7T1, exhibited similar activities to standard antagonists in modulating SOCS2 activity. Furthermore, MM/PBSA calculations were employed to assess the binding free energies of these proteins with SOCS2. Specifically, Lumbricin exhibited an average ΔGbinding of -59.25 kcal/mol, Chemoattractive glycoprotein ES20 showed -55.02 kcal/mol, and Lumbrokinase-7T1 displayed -69.28 kcal/mol. These values suggest strong binding affinities between these proteins and SOCS2, reinforcing their potential therapeutic efficacy in cardiovascular diseases. Further in vitro and animal studies are recommended to validate these findings and explore broader applications of Lumbricus-derived proteins.

Calculation of protein-ligand binding entropies using a rule-based molecular fingerprint

The use of fast in silico prediction methods for protein-ligand binding free energies holds significant promise for the initial phases of drug development. Numerous traditional physics-based models (e.g., implicit solvent models), however, tend to either neglect or heavily approximate entropic contributions to binding due to their computational complexity. Consequently, such methods often yield imprecise assessments of binding strength. Machine learning models provide accurate predictions and can often outperform physics-based models. They, however, are often prone to overfitting, and the interpretation of their results can be difficult. Physics-guided machine learning models combine the consistency of physics-based models with the accuracy of modern data-driven algorithms. This work integrates physics-based model conformational entropies into a graph convolutional network. We introduce a new neural network architecture (a rule-based graph convolutional network) that generates molecular fingerprints according to predefined rules specifically optimized for binding free energy calculations. Our results on 100 small host-guest systems demonstrate significant improvements in convergence and preventing overfitting. We additionally demonstrate the transferability of our proposed hybrid model by training it on the aforementioned host-guest systems and then testing it on six unrelated protein-ligand systems. Our new model shows little difference in training set accuracy compared to a previous model but an order-of-magnitude improvement in test set accuracy. Finally, we show how the results of our hybrid model can be interpreted in a straightforward fashion.

Exploring the conformational dynamics and key amino acids in the CD26-caveolin-1 interaction and potential therapeutic interventions

This study aimed to decipher the interaction between CD26 and caveolin-1, key proteins involved in cell signaling and linked to various diseases. Using computational methods, we predicted their binding conformations and assessed stability through 100 ns molecular dynamics (MD) simulations. We identified two distinct binding conformations (con1 and con4), with con1 exhibiting superior stability. In con1, specific amino acids in CD26, namely GLU237, TYR241, TYR248, and ARG147, were observed to engage in interactions with the F-J chain of Caveolin-1, establishing hydrogen bonds and cation or π-π interactions. Meanwhile, in con4, CD26 amino acids ARG253, LYS250, and TYR248 interacted with the J chain of Caveolin-1 via hydrogen bonds, cation-π interactions, and π-π interactions. Virtual screening also revealed potential small-molecule modulators, including Crocin, Poliumoside, and Canagliflozin, that could impact this interaction. Additionally, predictive analyses were conducted on the potential bioactivity, drug-likeness, and ADMET properties of these three compounds. These findings offer valuable insights into the binding mechanism, paving the way for new therapeutic strategies. However, further validation is required before clinical application. In summary, we provide a detailed understanding of the CD26 and caveolin-1 interaction, identifying key amino acids and potential modulators, essential for developing targeted therapies.

SurfPB: A GPU-Accelerated Electrostatic Calculation and Visualization Tool for Biomolecules

In this work, we present SurfPB as a useful tool for the study of biomolecules. It can do many typical calculations, including the molecular surface, electrostatic potential, solvation free energy, entropy, and binding free energy. Among all of the calculations, the entropy calculation is the most time-consuming one. In SurfPB, the calculation can be performed in a vacuum or implicit solvent and accelerated on GPU. The Poisson-Boltzmann equation solver is accelerated on GPU as well. Moreover, we developed a graphical user interface for SurfPB. It allows users to input the parameters and complete the whole calculation in a visual way. The calculated electrostatic potentials are shown on the molecular surface in a three-dimensional scene.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: I. Standard procedure

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein-ligand binding, the standard end-point calculations cannot produce useful outcomes in host-guest binding and thus are not recommended unless alterations are performed.