Binding affinity estimation from restrained umbrella sampling simulations

Probing the Epidermal Growth Factor Receptor under Piconewton Mechanical Compressive Force Manipulations

Studying the relationship among protein structure, dynamics, and function under external compressive forces offers valuable insights. While extensive research has focused on manipulating protein dynamics and ligand-receptor interactions under pulling forces, the exploration of protein conformational changes under compressive forces has been limited. In this study, we investigate the response of unliganded epidermal growth factor receptor (EGFR) monomers, liganded EGF-EGFR monomers, and dimers when exposed to external compressive forces using a home-modified AFM setup with an ultrasoft AFM tip. We observed that both ligand-bound and unbound EGFR proteins can undergo spontaneous tertiary structural rupture under piconewton-level compressive forces, a previously hidden protein behavior that may play a significant role in protein cell signaling. The magnitudes of the threshold compressive forces obtained in our study lie in the range of tens and hundreds of piconewtons (pN), which is accessible within a live biological system. Moreover, we developed a kinetic model to exhibit that only a fraction of the uniaxial compressive force exerted by the AFM tip affects the internal tension that causes a pseudopulling force within the protein before it undergoes the tertiary structural rupture. This calculated fraction ranged from 0.45 to 0.65, depending on the protein type and the approach velocity of the AFM tip. Additionally, we employed molecular dynamics (MD) simulations, particularly Steered MD (SMD) simulations along with Umbrella Sampling (US), to investigate the dynamics of unliganded and liganded EGFR in the presence of external compressive forces. These MD simulation results offer valuable insights into the flexibilities and unfolding behaviors of both liganded and unliganded EGFR proteins when subjected to external compressive forces.

Exploring novel and potent glycogen synthase kinase-3β inhibitors through systematic drug designing approach

Significant implications of glycogen synthase kinase-3β (GSK-3β) have been reported in various neuronal disorders and malignant cancers. GSK-3β modulates diverse protein targets through phosphorylation, and its aberrant activity leads to neurological complications as well as tumour onset. Therefore, inhibiting GSK-3β activity through active-site fitting molecules may offer a favourable strategy for intercepting these disorders. This comprehensive study used multiple assays in tandem in order to explore the most potent GSK-3β inhibitor. Following structural similarity screening, 135 molecular docking and 135 standard MM-GBSA experiments were performed using AZD1080, a known inhibitor as standard. Among the 32 molecules demonstrating a stronger binding affinity than reference, only the two most potent molecules were chosen and their binding free energy was compared to AZD1080 using the Desmond trajectory clustering and eventual MM-GBSA. Additionally, the interaction status of these molecules and AZD1080 with GSK-3β was explored post-molecular dynamics. The stability of the strongest molecule (most potent) was evaluated in the active site of the above-mentioned kinase keeping its apo-form as reference. Notably, the e-Pharmacophores mapping was performed to address the level of complementarity of the most potent molecule and AZD1080 with the functional site of GSK-3β. Using various techniques, we identified the molecule with PubChem CID: 11167509 as the strongest molecule for obstructing GSK-3β, which may serve as a promising therapeutic after the meticulous evaluation on diverse models.

Viral entry mechanisms: the role of molecular simulation in unlocking a key step in viral infections

Viral infections are a major global health concern, affecting millions of people each year. Viral entry is one of the crucial stages in the infection process, but its details remain elusive. Enveloped viruses are enclosed by a lipid membrane that protects their genetic material and these viruses are linked to various human illnesses, including influenza, and COVID-19. Due to the advancements made in the field of molecular simulation, significant progress has been made in unraveling the dynamic processes involved in viral entry of enveloped viruses. Simulation studies have provided deep insight into the function of the proteins responsible for attaching to the host receptors and promoting membrane fusion (fusion proteins), deciphering interactions between these proteins and receptors, and shedding light on the functional significance of key regions, such as the fusion peptide. These studies have already significantly contributed to our understanding of this critical aspect of viral infection and assisted the development of effective strategies to combat viral diseases and improve global health. This review focuses on the vital role of fusion proteins in facilitating the entry process of enveloped viruses and highlights the contributions of molecular simulation studies to uncover the molecular details underlying their mechanisms of action.

Understanding Folding of bFGF and Potential Cellular Protective Mechanisms of Neural Cells

Traumatic brain injury (TBI) is a serious health condition that affects an increasing number of people, especially veterans and athletes. TBI causes serious consequences because of its long-lasting impact on the brain and its alarming frequency of occurrence. Although the brain has some natural protective mechanisms, the processes that trigger them are poorly understood. Fibroblast growth factor (FGF) proteins interact with receptor proteins to protect cells. Gaps in the literature include how basic-FGF (bFGF) is activated by heparin, can heparin-like molecules induce neural protection, and the effect of allosteric binding on bFGF activity. To fill the gap in our understanding, we applied temperature replica exchange to study the influence of heparin binding to bFGF and how mutations in bFGF influence stability. A new favorable binding site was identified by comparing free energies computed from the potential of mean force (PMF). Although the varied sugars studied resulted in different interactions with bFGF compared to heparin, they each produced structural effects similar to those of bFGF that likely facilitate receptor binding and signaling. Our results also demonstrate how point mutations can trigger the same conformational change that is believed to promote favorable interactions with the receptor. A deeper atomic-level understanding of how chemicals are released during TBI is needed to improve the development of new treatments for TBI and could contribute to a better understanding of other diseases.

Discovery of a heparan sulfate binding domain in monkeypox virus H3 as an anti-poxviral drug target combining AI and MD simulations

Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.

Tutorial on Umbrella Sampling Simulation with a Combined QM/MM Potential: The Potential of Mean Force for an SN2 Reaction in Water

We present a tutorial to carry out umbrella-sampling free-energy simulations with a combined quantum mechanical and molecular mechanical (QM/MM) potential, which may also be used in a computational or biophysical chemistry curriculum for first-year graduate and undergraduate students. In this article, we choose the Type II SN2 Menshutkin reaction between ammonia and chloromethane to construct the potential of mean force (PMF) for the reaction in aqueous solution. In this exercise, we wish to accomplish three tasks: (1) an understanding of the concept of PMF and the umbrella-sampling free-energy simulation method, (2) the use of a combined QM/MM potential in molecular dynamics simulation of chemical reactions, and (3) an understanding of solvent effects and intermolecular interactions on chemical reactions through comparison with gas-phase results. Analysis of the simulation results allows students to appreciate the difference between the transition state along a PMF from statistical simulations versus the optimized saddle point in the gas phase, the shift of transition state with respect to solvation, and the significance of electronic polarization. Through this tutorial, students will gain the necessary skills to carry out free energy simulations of chemical reactions in solution and in enzymes both in a classroom and in the laboratory.

Driving forces and large scale affinity calculations for piperine/γ-cyclodxetrin complexes: Mechanistic insights from umbrella sampling simulation and DFT calculations

Piperine (PiP), a bioactive molecule, exhibits numerous health benefits and is frequently employed as a co-delivery agent with various phytomedicines (e.g., curcumin) to enhance their bioavailability. This is attributed to PiP's inhibitory activity against drug-metabolizing proteins, notably CYP3A4. Nevertheless, PiP encounters solubility challenges addressed in this study using cyclodextrins (CDs). Specifically, γ-CD and its derivatives, Hydroxypropyl-γ-CD (HP-γ-CD), and Octakis (6-O-sulfo)-γ-CD (Octakis-S-γ-CD), were employed to form supramolecular complexes with PiP. The conformational space of the complexes was assessed through 1 μs molecular dynamics simulations and umbrella sampling. Additionally, quantum mechanical calculations using wB97X-D dispersion-corrected DFT functional and 6-311 + G(d,p) basis set were conducted on the complexes to examine the thermodynamics and kinetic stability. Results indicated that Octakis-S-γ-CD exhibits superior host capabilities for PiP, with the most favorable complexation energy (-457.05 kJ/mol), followed by HP-γ-CD (-249.16 kJ/mol). Furthermore, two conformations of the Octakis-S-γ-CD/PiP complex were explored to elucidate the optimal binding orientation of PiP within the binding pocket of Octakis-S-γ-CD. Supramolecular chemistry relies significantly on non-covalent interactions. Therefore, our investigation extensively explores the critical atoms involved in these interactions, elucidating the influence of substituted groups on the stability of inclusion complexes. This comprehensive analysis contributes to emphasizing the γ-CD derivatives with improved host capacity.

Overcoming Sampling Issues and Improving Computational Efficiency in Collective-Variable-Based Enhanced-Sampling Simulations: A Tutorial

This tutorial is designed to help users overcome sampling challenges and improve computational efficiency in collective-variable (CV)-based enhanced-sampling, or importance-sampling, simulations. Toward this end, we introduce well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) and its integration with Gaussian accelerated molecular dynamics (GaMD). Additionally, use will be made of a method for identifying the least-free-energy pathway (LFEP) and multiple concurrent pathways on high-dimensional free-energy surfaces. We illustrate these sampling techniques with the conformational equilibria of trialanine and chignolin in aqueous solution as test cases. This tutorial assumes that the user has prior experience with molecular dynamics (MD) simulations, in general, with the popular program NAMD, and to some extent with Colvars, the module for CV-based calculations. This tutorial can, however, in large measure be used in conjunction with alternate MD engines that support the Colvars module such as GROMACS, LAMMPS, and Tinker-HP.

On the Control of Directionality of Myosin

The origin of the unique directionality of myosin has been a problem of fundamental and practical importance. This work establishes in a conclusive way that the directionality is controlled by tuning the barrier for the rate-determining step, namely, the ADP release step. This conclusion is based on exploring the molecular origin behind the reverse directionality of myosins V and VI and the determination of the origin of the change in the barriers of the ADP release for the forward and backward motions. Our investigation is performed by combining different simulation methods such as steer molecular dynamics (SMD), umbrella sampling, renormalization method, and automated path searching method. It is found that in the case of myosin V, the ADP release from the postrigor (trailing head) state overcomes a lower barrier than the prepowerstroke (leading head) state, which is also evident from experimental observation. In the case of myosin VI, we noticed a different trend when compared to myosin V. Since the directionality of myosins V and VI follows a reverse trend, we conclude that such differences in the directionality are controlled by the free energy barrier for the ADP release. Overall, the proof that the directionality of myosin is determined by the activation barrier of the rate-determining step in the cycle, rather than by some unspecified dynamical effects, has general importance.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.

Exploring endocrine FGFs - structures, functions and biomedical applications

The family of fibroblast growth factors (FGFs) consists of 22 members with diverse biological functions in cells, from cellular development to metabolism. The family can be further categorized into three subgroups based on their three modes of action. FGF19, FGF21, and FGF23 are endocrine FGFs that act in a hormone-like/endocrine manner to regulate various metabolic activities. However, all three members of the endocrine family require both FGF receptors (FGFRs) and klotho co-receptors to elicit their functions. α-klotho and β-klotho act as scaffolds to bring endocrine FGFs closer to their receptors (FGFRs) to form active complexes. Numerous novel studies about metabolic FGFs' structures, mechanisms, and physiological insights have been published to further understand the complex molecular interactions and physiological activities of endocrine FGFs. Herein, we aim to review the structures, physiological functions, binding mechanisms to cognate receptors, and novel biomedical applications of endocrine FGFs in recent years.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

An Ensemble Docking Approach for Analyzing and Designing Aptamer Heterodimers Targeting VEGF165

Vascular endothelial growth factor 165 (VEGF165) is a prominent isoform of the VEGF-A protein that plays a crucial role in various angiogenesis-related diseases. It is homodimeric, and each of its monomers is composed of two domains connected by a flexible linker. DNA aptamers, which have emerged as potent therapeutic molecules for many proteins with high specificity and affinity, can also work for VEGF165. A DNA aptamer heterodimer composed of monomers of V7t1 and del5-1 connected by a flexible linker (V7t1:del5-1) exhibits a greater binding affinity with VEGF165 compared to either of the two monomers alone. Although the structure of the complex formed between the aptamer heterodimer and VEGF165 is unknown due to the highly flexible linkers, gaining structural information will still be valuable for future developments. Toward this end of accessing structural information, we adopt an ensemble docking approach here. We first obtain an ensemble of structures for both VEGF165 and the aptamer heterodimer by considering both small- and large-scale motions. We then proceed through an extraction process based on ensemble docking, molecular dynamics simulations, and binding free energy calculations to predict the structures of the VEGF165/V7t1:del5-1 complex. Through the same procedures, we reach a new aptamer heterodimer that bears a locked nucleic acid-modified counterpart of V7t1, namely RNV66:del5-1, which also binds well with VEGF165. We apply the same protocol to the monomeric units V7t1, RNV66, and del5-1 to target VEGF165. We observe that V7t1:del5-1 and RNV66:del5-1 show higher binding affinities with VEGF165 than any of the monomers, consistent with experiments that support the notion that aptamer heterodimers are more effective anti-VEGF165 aptamers than monomeric aptamers. Among the five different aptamers studied here, the newly designed RNV66:del5-1 shows the highest binding affinity with VEGF165. We expect that our ensemble docking approach can help in de novo designs of homo/heterodimeric anti-angiogenic drugs to target the homodimeric VEGF165.

Binding affinity predictions with hybrid quantum-classical convolutional neural networks

Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. Accordingly, precise binding affinity prediction, enabling the accurate selection of suitable candidates from an extensive pool of potential compounds, can greatly reduce the expenses associated to practical experimental protocols. In this respect, recent advances revealed that deep learning methods show superior performance compared to other traditional computational methods, especially with the advent of large datasets. These methods, however, are complex and very time-intensive, thus representing an important clear bottleneck for their development and practical application. In this context, the emerging realm of quantum machine learning holds promise for enhancing numerous classical machine learning algorithms. In this work, we take one step forward and present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical counterpart while still maintaining optimal performance in the predictions. Additionally, this results in a significant cost and time savings of up to 40% in the training stage, which means a substantial speed-up of the drug design process.

Cholesterol in Class C GPCRs: Role, Relevance, and Localization

G-protein coupled receptors (GPCRs), one of the largest superfamilies of cell-surface receptors, are heptahelical integral membrane proteins that play critical roles in virtually every organ system. G-protein-coupled receptors operate in membranes rich in cholesterol, with an imbalance in cholesterol level within the vicinity of GPCR transmembrane domains affecting the structure and/or function of many GPCRs, a phenomenon that has been linked to several diseases. These effects of cholesterol could result in indirect changes by altering the mechanical properties of the lipid environment or direct changes by binding to specific sites on the protein. There are a number of studies and reviews on how cholesterol modulates class A GPCRs; however, this area of study is yet to be explored for class C GPCRs, which are characterized by a large extracellular region and often form constitutive dimers. This review highlights specific sites of interaction, functions, and structural dynamics involved in the cholesterol recognition of the class C GPCRs. We summarize recent data from some typical family members to explain the effects of membrane cholesterol on the structural features and functions of class C GPCRs and speculate on their corresponding therapeutic potential.