Explicit Characterization of the Free Energy Landscape of pKID-KIX Coupled Folding and Binding

Discovering Lassa virus nucleoprotein inhibitors via in silico drug repositioning approach

Lassa fever, caused by the zoonotic Lassa virus (LASV), poses a significant health threat in Africa, leading to thousands of infections and deaths annually and has the potential to spread to other parts of the world. Despite the urgency for effective treatments, there are currently no approved drugs or vaccines for Lassa fever. LASV possesses a unique negative-sense RNA genome, and NP plays a crucial role in viral assembly and infection. Crystallographic analysis reveals distinct domains in NP, with the N-terminal domain involved in RNA binding and the C-terminal domain exhibiting exoribonuclease activity, suppressing type I interferon-mediated immune responses. This study explores the potential of repurposing existing FDA-approved drugs by targeting the N-terminal domain of LASV's nucleoprotein (NP). Docking simulations and molecular dynamics experiments were conducted, revealing promising interactions between NP and widely used and well tolerated drugs such as metacycline, eltrombopag, glimepiride, lurasidone, paliperidone, prednisone, doxazosin, flavin mononucleotide, and pimozide. These drugs exhibited stable binding throughout 100 ns simulations, with interactions resembling those observed with the natural ligand, dTTP. Binding free energy calculations identified key amino acids, particularly Phe176 and Arg300, as crucial for drug-NP interactions. Notably, drugs like FMN, prednisone, metacycline, pimozide, and glimepiride displayed binding affinities comparable to dTTP, suggesting their potential as LASV inhibitors. The study underscores the importance of further experimental and clinical validation of these in silico findings. The identified drugs present promising candidates for potential treatments for Lassa fever, addressing the current gap in approved therapeutics for this life-threatening infectious disease.

AI-assisted generation and in-depth in-silico evaluation of potential inhibitor targeting aurora kinase A (AURKA): An anticancer discovery exploiting synthetic lethality approach

Genetic alterations are lead causative agents behind the complex pathologies of cancers which render all treatments unarmed. Such alterations in oncogenes can be treated by direct inhibition by specific drugs while alteration in tumor suppressor genes mediating loss of function is challenging to treat. Identification of synthetic lethal partners to specific tumor suppressor genes and mediating their inhibition can be a potential approach to deal with loss of function mutations. Aurora kinase A (AURKA) has been established as an effective synthetic lethal partner of several tumor suppressor genes and is overexpressed in cancerous conditions, mediating adverse pathologies. The present AI-assisted study deals with the generation of novel inhibitor compounds against AURKA and the exhaustive evaluation of the best compound using molecular docking, molecular dynamic simulation, MM/PBSA, and QM/MMGBSA-based analysis. Out of the 200 novel compounds generated using features of ATP binding pocket of AURKA and previously reported inhibitor, compound 1 (4-{5-fluoro-6-[(1Z)-3-hydrazinyl-3-oxo-2-phenylprop-1-en-1-yl]pyridin-2-yl}benzoic acid) was identified as the most potent candidate with high negative binding energy of -10.4 kcal/mol in molecular docking analysis. The molecular dynamic simulation analysis resulted in major conformational changes in the conserved DFG motif and loop 277-291 of AURKA in the apo-AURKA compared to AURKA-compound 1 complex thus maintaining open ATP binding cavity in apo-form and inhibiting the entry of ATP to its binding site in complex form. The free energy landscape displayed a persistence of folded states of the enzyme in complex form. The MM/PBSA revealed effective Gibb's free energy of binding of -11 kcal/mol for compound 1 inhibiting AURKA. The QM/MMGBSA analysis resulted in a significantly high negative binding energy of -13.98 kcal/mol proving significant inhibition potential of compound 1 against AURKA. Therefore, further in-vitro investigation can provide a novel effective, and safe treatment against a wide range of cancers by targeting a well-established cancer target AURKA.

Deciphering the Mystery in p300 Taz2-p53 TAD2 Recognition

Intrinsically disordered proteins (IDPs) engage in various fundamental biological activities, and their behavior is of particular importance for a better understanding of the verbose but well-organized signal transduction in cells. IDPs exhibit uniquely paradoxical features with low affinity but simultaneously high specificity in recognizing their binding targets. The transcription factor p53 plays a crucial role in cancer suppression, carrying out some of its biological functions using its disordered regions, such as N-terminal transactivation domain 2 (TAD2). Exploration of the binding and unbinding processes between proteins is challenging, and the inherently disordered properties of these regions further complicate the issue. Computer simulations are a powerful tool to complement the experiments to fill gaps to explore the binding/unbinding processes between proteins. Here, we investigated the binding mechanism between p300 Taz2 and p53 TAD2 through extensive molecular dynamics (MD) simulations using the physics-based UNited RESidue (UNRES) force field with additional Go̅-like potentials. Distance restraints extracted from the NMR-resolved structures were imposed on intermolecular residue pairs to accelerate binding simulations, in which Taz2 was immobilized in a native-like conformation and disordered TAD2 was fully free. Starting from six structures with TAD2 placed at different positions around Taz2, we observed a metastable intermediate state in which the middle helical segment of TAD2 is anchored in the binding pocket, highlighting the significance of the TAD2 helix in directing protein recognition. Physics-based binding simulations show that successful binding is achieved after a series of stages, including (1) protein collisions to initiate the formation of encounter complexes, (2) partial attachment of TAD2, and finally (3) full attachment of TAD2 to the correct binding pocket of Taz2. Furthermore, machine-learning-based PathDetect-SOM was used to identify two binding pathways, the encounter complexes, and the intermediate states.

Impact of Nonnative Interactions on the Binding Kinetics of Intrinsically Disordered p53 with MDM2: Insights from All-Atom Simulation and Markov State Model Analysis

Intrinsically disordered proteins (IDPs) lack a well-defined tertiary structure but are essential players in various biological processes. Their ability to undergo a disorder-to-order transition upon binding to their partners, known as the folding-upon-binding process, is crucial for their function. One classical example is the intrinsically disordered transactivation domain (TAD) of the tumor suppressor protein p53, which quickly forms a structured α-helix after binding to its partner MDM2, with clinical significance for cancer treatment. However, the contribution of nonnative interactions between the IDP and its partner to the rapid binding kinetics, as well as their interplay with native interactions, is not well understood at the atomic level. Here, we used molecular dynamics simulation and Markov state model (MSM) analysis to study the folding-upon-binding mechanism between p53-TAD and MDM2. Our results suggest that the system progresses from the nascent encounter complex to the well-structured encounter complex and finally reaches the native complex, following an induced-fit mechanism. We found that nonnative hydrophobic and hydrogen bond interactions, combined with native interactions, effectively stabilize the nascent and well-structured encounter complexes. Among the nonnative interactions, Leu25p53-Leu54MDM2 and Leu25p53-Phe55MDM2 are particularly noteworthy, as their interaction strength is close to the optimum. Evidently, strengthening or weakening these interactions could both adversely affect the binding kinetics. Overall, our findings suggest that nonnative interactions are evolutionarily optimized to accelerate the binding kinetics of IDPs in conjunction with native interactions.

Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics

Intrinsically disordered proteins participate in many biological processes by folding upon binding to other proteins. However, coupled folding and binding processes are not well understood from an atomistic point of view. One of the main questions is whether folding occurs prior to or after binding. Here we use a novel, unbiased, high-throughput adaptive sampling approach to reconstruct the binding and folding between the disordered transactivation domain of c-Myb and the KIX domain of the CREB-binding protein. The reconstructed long-term dynamical process highlights the binding of a short stretch of amino acids on c-Myb as a folded α-helix. Leucine residues, especially Leu298-Leu302, establish initial native contacts that prime the binding and folding of the rest of the peptide, with a mixture of conformational selection on the N-terminal region with an induced fit of the C-terminal.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

A theory of chemical reactions in biomolecules in solution: Generalized Langevin mode analysis (GLMA)

The generalized Langevin mode analysis (GLMA) is applied to chemical reactions in biomolecules in solution. The theory sees a chemical reaction in solution as a barrier-crossing process, similar to the Marcus theory. The barrier is defined as the crossing point of two free-energy surfaces that are attributed to the reactant and product of the reaction. It is assumed that both free-energy surfaces are quadratic or harmonic. The assumption is based on the Kim-Hirata theory of structural fluctuation of protein, which proves that the fluctuation around an equilibrium structure is quadratic with respect to the structure or atomic coordinates. The quadratic surface is a composite of many harmonic functions with different modes or frequencies. The height of the activation barrier will be dependent on the mode or frequency-the less the frequency, the lower the barrier. Hence, it is essential to decouple the fluctuational modes into a hierarchical order. GLMA is impeccable for this purpose. It is essential for a theoretical study of chemical reactions to choose a reaction coordinate along which the reaction proceeds. We suppose that the mode whose center of coordinate and/or the frequency changes most before and after the reaction is the one relevant to the chemical reaction and choose the coordinate as the reaction coordinate. The rate of reaction along the reaction coordinate is k_rate=ν⁡exp-ΔF^(†)/k_BT, which is similar to the Marcus expression for the electron transfer reaction. In the equation, ΔF^(†) is the activation barrier defined by ΔF^(†)≡F^(r)Q^†-F^(r)(Q_eq ^(r)), where F^(r)(Q_eq ^(r)) and F^(r)Q^† denote the free energies at equilibrium Q_eq ^(r) and the crossing point Q^†, respectively, both on the free energy surface of the reactant.

Fuzzy Drug Targets: Disordered Proteins in the Drug-Discovery Realm

Intrinsically disordered proteins (IDPs) and regions (IDRs) form a large part of the eukaryotic proteome. Contrary to the structure-function paradigm, the disordered proteins perform a myriad of functions in vivo. Consequently, they are involved in various disease pathways and are plausible drug targets. Unlike folded proteins, that have a defined structure and well carved out drug-binding pockets that can guide lead molecule selection, the disordered proteins require alternative drug-development methodologies that are based on an acceptable picture of their conformational ensemble. In this review, we discuss various experimental and computational techniques that contribute toward understanding IDP "structure" and describe representative pursuances toward IDP-targeting drug development. We also discuss ideas on developing rational drug design protocols targeting IDPs.

Structural screening into the recognition of a potent inhibitor against non-structural protein 16: a molecular simulation to inhibit SARS-CoV-2 infection

COVID-19 infection is caused by endemic crown infection (SARS-CoV-2) and is associated with lung damage and severe immune response. Non-Structural Proteins are the central components of coronaviral transcription and replication machinery in SARS-CoV-2 and also stimulate mRNA cap methylation to avoid the immune response. Non-Structural Protein 16 (NSP16) is one of the primary targets for the drug discovery of coronaviruses. Discovering an effective inhibitor against the NSP16 in comparison with Sinefungin was the main purpose of this investigation. Binding free-energy calculations, computational methods of molecular dynamics, docking, and virtual screening were utilized in this study. The ZINC and PubChem databases were applied to screen some chemical compounds regarding Sinefungin as a control inhibitor. Based on structural similarity to Sinefungin, 355 structures were obtained from the mentioned databases. Subsequently, this set of compounds were monitored by AutoDock Vina software, and ultimately the potent inhibitor (PUBCHEM512713) was chosen. At the next stage, molecular dynamics were carried out by GROMACS software to evaluate the potential elected compounds in a simulated environment and in a timescale of 100 nanoseconds. MM-PBSA investigation exhibited that the value of binding free energy for PUBCHEM512713 (-30.829 kJ.mol^-1) is more potent than Sinefungin (-11.941 kJ.mol^-1). Furthermore, the results of ADME analysis illustrated that the pharmacokinetics, drug-likeness, and lipophilicity parameters of PUBCHEM512713 are admissible for human utilization. Finally, our data suggested that PUBCHEM512713 is an effective drug candidate for inhibiting the NSP16 and is suitable for in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening for identification of potential non-steroidal LXR modulators against neurodegenerative conditions

Liver X Receptors (LXRs) are members of the nuclear receptor superfamily that regulate cholesterol metabolism. LXRs have been suggested as promising targets against many neurodegenerative diseases (NDDs). The present study was aimed to identify novel non-steroidal molecules that may potentially modulate LXR activity. The structure-based virtual screening (SBVS) was used to search for suitable compounds from the Asinex library. The top hits were selected and filtered based on their binding affinity for LXR α and β isoforms. Based on molecular docking and scoring results, 24 compounds were selected that had binding energy in the range of - 13.9 to - 12 for LXRα and - 12.5 to - 11 for LXRβ, which were higher than the reference ligands (GW3965 and TO901317). Further, the five hits referred to as model 29, 64, 202, 250, 313 were selected by virtue of their binding interactions with amino acid residues at the active site of LXRs. The selected hits were then subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis and blood-brain permeability prediction. It was observed that the selected hits had better pharmacokinetic properties with no toxicity and could cross blood-brain barrier. Further, the selected hits were analysed for dynamic evolution of the system with LXRs by molecular dynamics (MD) simulation at 100 ns using GROMACS. The MD simulation results validated that selected hits possess a remarkable amount of flexibility, stability, compactness, binding energy and exhibited limited conformational modification. The root mean square deviation (RMSD) values of the top-scoring hits complexed with LXRα and LXRβ were 0.05-0.6 nm and 0.05-0.45 nm respectively, which is greater than the protein itself. Altogether the study identified potential non-steroidal LXR modulators that appear to be effective against various neurodegenerative conditions involving perturbed cholesterol and lipid homeostasis.

All-Atom Simulations Uncover Structural and Dynamical Properties of STING Proteins in the Membrane System

Recent studies have shown that the stimulator of interferon gene (STING) protein plays a central role in the immune system by facilitating the production of type I interferons in cells. The STING signaling pathway is also a prominent activator of cancer-killing T cells that initiate a powerful adaptive immune response. Since biomolecular signaling pathways are complicated and not easily identified through traditional experiments, molecular dynamics (MD) has often been used to study structural and dynamical responses of biological pathways. Here, we carried out MD simulations for full-length chicken and human STING (chSTING and hSTING) proteins. Specifically, we investigated ligand-bound closed (holo) and ligand-unbound open (apo) forms of STING in the membrane system by comparing their conformational and dynamical differences. Our research provides clues for understanding the mechanism of the STING signaling pathway by uncovering detailed insights for the examined systems: the residues from each chain in the binding pocket are strongly correlated to one another in the open STING structure compared with those in the closed STING structure. Ligand-bound closed STING displays ∼174° rotation of the ligand-binding domain (LBD) relative to the open STING structure. The dynamical analysis of residue Cys148 located in the linker region of hSTING does not support the earlier hypothesis that Cys148 can form disulfide bonds between adjacent STING dimers. We also demonstrate that using the full-length proteins is critical, since the MD simulations of the LBD portion alone cannot properly describe the global conformational properties of STING.

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales─picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure-function relationships require atomistic knowledge of the populated states in the ensemble. In this Perspective, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

Site-Specific Backbone and Side-Chain Contributions to Thermodynamic Stabilizing Forces of the WW Domain

The native structure of a protein is stabilized by a number of interactions such as main-chain hydrogen bonds and side-chain hydrophobic contacts. However, it has been challenging to determine how these interactions contribute to protein stability at single amino acid resolution. Here, we quantified site-specific thermodynamic stability at the molecular level to extend our understanding of the stabilizing forces in protein folding. We derived the free energy components of individual amino acid residues separately for the folding of the human Pin WW domain based on simulated structures. A further decomposition of the thermodynamic properties into contributions from backbone and side-chain groups enabled us to identify the critical residues in the secondary structure and hydrophobic core formation, without introducing physical modifications to the system as in site-directed mutagenesis methods. By relating the structural and thermodynamic changes upon folding for each residue, we find that the simultaneous formation of the backbone hydrogen bonds and side-chain contacts cooperatively stabilizes the folded structure. The identification of stabilizing interactions in a folding protein at atomic resolution will provide molecular insights into understanding the origin of the protein structure and into engineering a more stable protein.

Shedding Light on the Inhibitory Mechanisms of SARS-CoV-1/CoV-2 Spike Proteins by ACE2-Designed Peptides

Angiotensin-converting enzyme 2 (ACE2) is the host cellular receptor that locks onto the surface spike protein of the 2002 SARS coronavirus (SARS-CoV-1) and of the novel, highly transmissible and deadly 2019 SARS-CoV-2, responsible for the COVID-19 pandemic. One strategy to avoid the virus infection is to design peptides by extracting the human ACE2 peptidase domain α1-helix, which would bind to the coronavirus surface protein, preventing the virus entry into the host cells. The natural α1-helix peptide has a stronger affinity to SARS-CoV-2 than to SARS-CoV-1. Another peptide was designed by joining α1 with the second portion of ACE2 that is far in the peptidase sequence yet grafted in the spike protein interface with ACE2. Previous studies have shown that, among several α1-based peptides, the hybrid peptidic scaffold is the one with the highest/strongest affinity for SARS-CoV-1, which is comparable to the full-length ACE2 affinity. In this work, binding and folding dynamics of the natural and designed ACE2-based peptides were simulated by the well-known coarse-grained structure-based model, with the computed thermodynamic quantities correlating with the experimental binding affinity data. Furthermore, theoretical kinetic analysis of native contact formation revealed the distinction between these processes in the presence of the different binding partners SARS-CoV-1 and SARS-CoV-2 spike domains. Additionally, our results indicate the existence of a two-state folding mechanism for the designed peptide en route to bind to the spike proteins, in contrast to a downhill mechanism for the natural α1-helix peptides. The presented low-cost simulation protocol demonstrated its efficiency in evaluating binding affinities and identifying the mechanisms involved in the neutralization of spike-ACE2 interaction by designed peptides. Finally, the protocol can be used as a computer-based screening of more potent designed peptides by experimentalists searching for new therapeutics against COVID-19.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.

Binding and folding in transcriptional complexes

Transcription factors are among the classes of proteins with the highest levels of disorder. Investigation of these regulatory proteins is uncovering not just the mechanisms that underlie gene regulation, but relationships that apply to all intrinsically disordered proteins. Recent studies confirm that binding does not necessarily induce folding but that when it does, it tends to follow induced fit mechanisms. Other work emphasises the importance of electrostatics to interactions involving intrinsically disordered proteins, and roles of intrinsic disorder in phase transitions. All these features help direct transcription factors to target sites in the genome to upregulate or downregulate transcription.

Phosphorylation regulates the binding of intrinsically disordered proteins via a flexible conformation selection mechanism

Phosphorylation is one of the most common post-translational modifications. The phosphorylation of the kinase-inducible domain (KID), which is an intrinsically disordered protein (IDP), promotes the folding of KID and binding with the KID-interacting domain (KIX). However, the regulation mechanism of the phosphorylation on KID is still elusive. In this study, the structural ensembles and binding process of pKID and KIX are studied by all-atom enhanced sampling technologies. The results show that more hydrophobic interactions are formed in pKID, which promote the formation of the special hydrophobic residue cluster (HRC). The pre-formed HRC promotes binding to the correct sites of KIX and further lead the folding of pKID. Consequently, a flexible conformational selection model is proposed to describe the binding and folding process of intrinsically disordered proteins. The binding mechanism revealed in this work provides new insights into the dynamic interactions and phosphorylation regulation of proteins.

Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations

The exploration of intrinsically disordered proteins in isolation is a crucial step to understand their complex dynamical behavior. In particular, the emergence of partially ordered states has not been explored in depth. The experimental characterization of such partially ordered states remains elusive due to their transient nature. Molecular dynamics mitigates this limitation thanks to its capability to explore biologically relevant timescales while retaining atomistic resolution. Here, millisecond unbiased molecular dynamics simulations were performed in the exemplar N-terminal region of p53. In combination with state-of-the-art Markov state models, simulations revealed the existence of several partially ordered states accounting for [Formula: see text] 40% of the equilibrium population. Some of the most relevant states feature helical conformations similar to the bound structure of p53 to Mdm2, as well as novel [Formula: see text]-sheet elements. This highlights the potential complexity underlying the energy surface of intrinsically disordered proteins.