To establish a procedure for screening compounds that inhibit ligand–receptor binding, we employed a multidimensional virtual-system coupled molecular dynamics (mD-VcMD), a generalized ensemble method recently developed by our group. In this approach, each compound is initially placed far from the receptor. Both receptor and compound were fully flexible in explicit solvent during sampling. The mD-VcMD generated a free-energy landscape of the compound–receptor interactions, in which a probability of existence was assigned to each sampled conformation. We examined four compounds that bind to the papain-like protease (PLpro) of SARS-CoV-2. The resultant free-energy landscapes were funnel-like for all compounds. The probabilities assigned to the free-energy basins correlated well with the measured dissociation constants. Furthermore, structural clustering revealed two types of binding modes within the free-energy basin. The probabilities assigned to the binding modes correlated well with the measured enzyme inhibitory activity. These results suggest that the proposed procedure is effective for selecting candidate inhibitors among the examined compounds.

DNA-targeting drugs exploit structural features of DNA, including base stacking, grooves, and noncanonical structures, to bind and modulate DNA function. Similarly, DNA aptamers leverage unique physicochemical environments created by diverse DNA conformations to achieve high-affinity recognition of small molecules, making them critical for biosensing applications. Despite their therapeutic and sensing potential, accurately predicting binding configurations within these dynamic structures remains a significant computational challenge requiring advanced molecular dynamics (MD) simulations powered by modern force fields. To evaluate modern AMBER-based parametrizations across diverse structural motifs, including aptamers, duplexes, and quadruplex-duplex hybrids, we performed dynamic docking simulations of five DNA-ligand pairs using multicanonical MD, a generalized-ensemble method, across five distinct force fields (OL15, OL21, OL24, bsc1, and tumuc1). Analysis of 750 μs of trajectory data revealed significant variations in conformational ensembles. OL24 exhibited the highest accuracy in reproducing experimental structures based on our R-value analysis, which quantifies the ligand-DNA native contacts, while OL15, OL21, and bsc1 also demonstrated robust performance across all systems. In contrast, tumuc1 displayed a persistent bias toward distorted or misoriented conformations with low native-state populations, compromising structural reliability regardless of the system type. These findings provide critical insights for developing next-generation DNA force fields capable of accurately modeling non-native structures and enabling balanced sampling essential for predicting ligand binding in diverse biological contexts.

Molecular dynamics (MD) simulations are increasingly important for analyzing RNA-ligand interactions, particularly in the context of therapeutic development. However, the accuracy of RNA force fields remains insufficiently assessed, partly due to the limited sampling efficiency of MD approaches and the lack of reliable docking protocols. To evaluate the performance of modern AMBER-based RNA force fields, we selected four small RNA-ligand complexes from the Protein Data Bank (PDB) and executed dynamic docking simulations using one of the generalized ensemble methods, multicanonical MD, across five different force fields. We analyzed a total of 600 μs of simulation data, each reweighted to the canonical ensemble at physiological temperature. The resulting conformational ensembles varied across force fields for three of the four targets. Among the tested force fields, the parm99χOL3-vdWbb yielded the most accurate results based on our R-value analysis that measures the ligand-RNA native contacts, assuming the PDB structures represent the correct native conformations. However, further analysis revealed that some metastable, non-native RNA conformations had smaller intercalation sites with a closed binding pocket, resulting in shrinkage of the RNA molecules. These findings suggest that current RNA force fields may overstabilize non-native, closed conformations. The present simulations, methodology, analyses, and data offer valuable insights to guide the development of next-generation RNA force fields to better assess non-native RNA conformations.

Ligand-receptor docking simulation is difficult when the biomolecules have high intrinsic flexibility. If some knowledge on the ligand-receptor complex structure or inter-molecular contact sites are presented in advance, the difficulty of docking problem considerably decreases. This paper proposes a generalized-ensemble method "cartesian-space division mD-VcMD" (or CSD-mD-VcMD), which calculates stable complex structures without assist of experimental knowledge on the complex structure. This method is an extension of our previous method that requires the knowledge on the ligand-receptor complex structure in advance. Both the present and previous methods enhance the conformational sampling, and finally produce a binding free-energy landscape starting from a completely dissociated conformation, and provide a free-energy landscape. We applied the present method to same system studied by the previous method: A ligand (ribocil A or ribocil B) binding to an RNA (the aptamer domain of the FMN riboswitch). The two methods produced similar results, which explained experimental data. For instance, ribocil B bound to the aptamer's deep binding pocket more strongly than ribocil A did. However, this does not mean that two methods have a similar performance. Note that the present method did not use the experimental knowledge of binding sites although the previous method was supported by the knowledge. The RNA-ligand binding site could be a cryptic site because RNA and ligand are highly flexible in general. The current study showed that CSD-mD-VcMD is actually useful to obtain a binding free-energy landscape of a flexible system, i.e., the RNA-ligand interacting system.

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.

To investigate the effects of phosphorylation on the function of the human positive cofactor 4 (PC4), an enhanced molecular dynamics (MD) simulation was performed. The simulation system consists of the N-terminal intrinsic disordered region (IDR) of PC4 and a complex that comprises the C-terminal acidic activation domain of a herpes simplex virion protein 16 (VP16ad) and a homodimer of the C-terminal structured core domain of PC4 (PC4ctd). An earlier report of an experimental study reported that the PC4-VP16ad interaction is modulated by incremental phosphorylation of the IDR. That report also proposed a dynamic model where phosphorylated serine residues of a segment (SEAC) in the IDR contact positively charged residues (lysin and arginine) of another segment (K-rich) in the IDR. This contact formation induced by the phosphorylation results in variation of PC4-VP16ad interaction. However, this contact formation has not yet been measured directly because it is transiently formed and because the SEAC and K-rich segments are unstructured with high flexibility. We performed two simulations to mimic the incremental phosphorylation: the IDR was not phosphorylated in one simulation and only partially phosphorylated in the other. Our simulation results indicate that the phosphorylation weakens the IDR-VP16ad contact considerably with the induction of a compact structure in the IDR. This structure was stabilized by electrostatic interactions between the phosphorylated serine residues of a segment and lysine or arginine residues of another segment in the IDR, but the conformational fluctuation of this compact structure was considerably large. Consequently, the present study supports the experimentally proposed dynamic model. Results of this study can be important for computational elucidation of the functional modulation of PC4.

A small and flexible molecule, ribocil A (non-binder) or B (binder), binds to the deep pocket of the aptamer domain of the FMN riboswitch, which is an RNA molecule. This binding was studied by mD-VcMD, which is a generalized-ensemble simulation method. Ribocil A and B are structurally similar because they are optical isomers to each other. In the initial conformation of simulation, the ligands and the aptamer were completely dissociated in explicit solvent. The aptamer–ribocil B binding was stronger than the aptamer–ribocil A binding, which agrees with experiments. The computed free-energy landscape for the aptamer–ribocil B binding was funnel-like, whereas that for the aptamer–ribocil A binding was rugged. When passing through the gate (named “front gate”) of the binding pocket, each ligand interacted with bases of the riboswitch by non-native π-π stackings, and the stackings restrained the ligand’s orientation to be advantageous to reach the binding site smoothly. When the ligands reached the binding site in the pocket, the non-native stackings were replaced by the native stackings. The ligand’s orientation restriction is discussed referring to a selection mechanism reported in an earlier work on a drug–GPCR interaction. The present simulation showed another pathway leading the ligands to the binding site. The gate (“rear gate”) for this pathway was located completely opposite to the front gate on the aptamer’s surface. However, the approach from the rear gate required overcoming a free-energy barrier regarding ligand’s rotation before reaching the binding site.

We introduce a simple cutoff-based method for precise electrostatic energy calculations in the molecular dynamics (MD) simulations of point-particle systems. Our method employs a theoretically derived smooth pair potential function to define electrostatic energy, offering stability and computational efficiency in MD simulations. Instead of imposing specific physical conditions, such as dielectric environments or charge neutrality, we focus on the relationship represented by a single summation formula of charge-weighted pair potentials. This approach allows an accurate energy approximation for each particle, enabling a straightforward error analysis. The resulting particle-dependent pair potential captures the charge distribution information, making it suitable for heterogeneous systems and ensuring an enhanced accuracy through distant information inclusion. Numerical investigations of the Madelung constants of crystalline systems validate the method's accuracy.

Thymic selection and peripheral activation of conventional T (Tconv) and regulatory T (Treg) cells depend on TCR signaling, whose anomalies are causative of autoimmunity. Here, we expressed in normal mice mutated ZAP-70 molecules with different affinities for the CD3 chains, or wild type ZAP-70 at graded expression levels under tetracycline-inducible control. Both manipulations reduced TCR signaling intensity to various extents and thereby rendered those normally deleted self-reactive thymocytes to become positively selected and form a highly autoimmune TCR repertoire. The signal reduction more profoundly affected Treg development and function because their TCR signaling was further attenuated by Foxp3 that physiologically repressed the expression of TCR-proximal signaling molecules, including ZAP-70, upon TCR stimulation. Consequently, the TCR signaling intensity reduced to a critical range generated pathogenic autoimmune Tconv cells and concurrently impaired Treg development/function, leading to spontaneous occurrence of autoimmune/inflammatory diseases, such as autoimmune arthritis and inflammatory bowel disease. These results provide a general model of how altered TCR signaling evokes autoimmune disease.

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

A GA-guided multidimensional virtual-system coupled molecular dynamics (GA-mD-VcMD) simulation was conducted to elucidate binding mechanisms of a middle-sized flexible molecule, bosentan, to a GPCR protein, human endothelin receptor type B (hETB). GA-mD-VcMD is a generalized ensemble method that produces a free-energy landscape of the ligand-receptor binding by searching large-scale motions accompanied with stable maintenance of the fragile cell-membrane structure. All molecular components (bosentan, hETB, membrane, and solvent) were represented with an all-atom model. Then sampling was conducted from conformations where bosentan was distant from the binding site in the hETB binding pocket. The deepest basin in the resultant free-energy landscape was assigned to native-like complex conformation. The following binding mechanism was inferred. First, bosentan fluctuating randomly in solution is captured using a tip region of the flexible N-terminal tail of hETB via nonspecific attractive interactions (fly casting). Bosentan then slides occasionally from the tip to the root of the N-terminal tail (ligand–sliding). During this sliding, bosentan passes the gate of the binding pocket from outside to inside of the pocket with an accompanying rapid reduction of the molecular orientational variety of bosentan (orientational selection). Last, in the pocket, ligand–receptor attractive native contacts are formed. Eventually, the native-like complex is completed. The bosentan-captured conformations by the tip-region and root-region of the N-terminal tail correspond to two basins in the free-energy landscape. The ligand-sliding corresponds to overcoming of a free-energy barrier between the basins.

A heterocyclic compound mS-11 is a helix-mimetic designed to inhibit binding of an intrinsic disordered protein neural restrictive silence factor/repressor element 1 silencing factor (NRSF/REST) to a receptor protein mSin3B. We apply a generalized ensemble method, multi-dimensional virtual-system coupled molecular dynamics developed by ourselves recently, to a system consisting of mS-11 and mSin3B, and obtain a thermally equilibrated distribution, which is comprised of the bound and unbound states extensively. The lowest free-energy position of mS-11 coincides with the NRSF/REST position in the experimentally-determined NRSF/REST-mSin3B complex. Importantly, the molecular orientation of mS-11 is ordering in a wide region around mSin3B. The resultant binding scenario is: When mS-11 is distant from the binding site of mSin3B, mS-11 descends the free-energy slope toward the binding site maintaining the molecular orientation to be advantageous for binding. Then, finally a long and flexible hydrophobic sidechain of mS-11 fits into the binding site, which is the lowest-free-energy complex structure inhibiting NRSF/REST binding to mSin3B.

Quantifying the cell permeability of cyclic peptides is crucial for their rational drug design. However, the reasons remain unclear why a minor chemical modification, such as the difference between Ras inhibitors cyclorasin 9A5 and 9A54, can substantially change a peptide's permeability. To address this question, we performed enhanced sampling simulations of these two 11-mer peptides using the coupled Nosé-Hoover equation (cNH) we recently developed. The present cNH simulations realized temperature fluctuations over a wide range (240-600 K) in a dynamic manner, allowing structural samplings that were well validated by nuclear Overhauser effect measurements. The derived structural ensembles were comprehensively analyzed by all-atom structural clustering, mapping the derived clusters onto principal components (PCs) that characterize the cyclic structure, and calculating cluster-dependent geometric and chemical properties. The planar-open conformation was dominant in aqueous solvent, owing to inclusion of the Trp side chain in the main-chain ring, while the compact-closed conformation, which favors cell permeation due to its compactness and high polarity, was also accessible. Conformation-dependent cell permeability was observed in one of the derived PCs, demonstrating that decreased cell permeability in 9A54 is due to the high free energy barrier separating the two conformations. The origin of the change in free energy surface was determined to be loss of flexibility in the modified residues 2-3, resulting from the increased bulkiness of their side chains. The derived molecular mechanism of cell permeability highlights the significance of complete structural dynamics surveys for accelerating drug development with cyclic peptides.

We have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

A preceding experiment suggested that a compound, which inhibits binding of the REST/NRSF segment to the cleft of a receptor protein mSin3B, can be a potential drug candidate to ameliorate many neuropathies. We have recently developed an enhanced conformational sampling method, genetic-algorithm-guided multi-dimensional virtual-system-coupled canonical molecular dynamics, and in the present study, applied it to three systems consisting of mSin3B and one of three compounds, sertraline, YN3, and acitretin. Other preceding experiments showed that only sertraline inhibits the binding of REST/NRSF to mSin3B. The current simulation study produced the spatial distribution of the compounds around mSin3B, and showed that sertraline and YN3 bound to the cleft of mSin3B with a high propensity, although acitretin did not. Further analyses of the simulation data indicated that only the sertraline-mSin3B complex produced a hydrophobic core similar to that observed in the molecular interface of the REST/NRSF-mSin3B complex: An aromatic ring of sertraline sunk deeply in the mSin3B's cleft forming a hydrophobic core contacting to hydrophobic amino-acid residues located at the bottom of the cleft. The present study proposes a step to design a compound that inhibits competitively the binding of a ligand to its receptor.

Antibody based bio-molecular drugs are an exciting, new avenue of drug development as an alternative to the more traditional small chemical compounds. However, the binding mechanism and the effect on the conformational ensembles of a therapeutic antibody to its peptide or protein antigen have not yet been well studied. We have utilized dynamic docking and path sampling simulations based on all-atom molecular dynamics to study the binding mechanism between the antibody solanezumab and the peptide amyloid-β (Aβ). Our docking simulations reproduced the experimental structure and gave us representative binding pathways, from which we accurately estimated the binding free energy. Not only do our results show why solanezumab has an explicit preference to bind to the monomeric form of Aβ, but that upon binding, both molecules are stabilized towards a specific conformation, suggesting that their complex formation follows a novel, mutual population-shift model, where upon binding, both molecules impact the dynamics of their reciprocal one.

The molecular dynamics (MD) method is a promising approach for investigating the molecular mechanisms of microscopic phenomena. In particular, generalized ensemble MD methods can efficiently explore the conformational space with a rugged free-energy surface. However, the implementation and acquisition of technical knowledge for each generalized ensemble MD method are not straightforward for end-users. Here, we present a new version of the myPresto/omegagene software, which is an MD simulation engine tailored for a series of generalized ensemble methods, which are virtual-system coupled multicanonical MD (V-McMD), virtual-system coupled adaptive umbrella sampling (V-AUS), and virtual-system coupled canonical MD (VcMD). This program has been applied in several studies analyzing free-energy landscapes of a variety of molecular systems with all-atom simulations. The updated version provides new functionality for coarse-grained simulations powered by the hydrophobicity scale method. The software package includes a step-by-step tutorial document for enhanced conformational sampling of the poly-glutamine (poly-Q) oligomer expressed as a one-bead per residue model. The myPresto/omegagene software is freely available at the following URL: https://github.com/kotakasahara/omegagene under the Apache2 license.

ABSTRACTEnhanced conformational sampling, a genetic-algorithm-guided multi-dimensional virtual-system coupled molecular dynamics, can provide equilibrated conformational distributions of a receptor protein and a flexible ligand at room temperature. The distributions provide not only the most stable but also semi-stable complex structures, and propose a ligand–receptor binding process. This method was applied to a system consisting of a receptor protein, 14-3-3ε, and a flexible peptide, phosphorylated Myeloid leukemia factor 1 (pMLF1). The results present comprehensive binding pathways of pMLF1 to 14-3-3ε. We identified four thermodynamically stable clusters of MLF1 on the 14-3-3ε surface, and free-energy barriers among some clusters. The most stable cluster includes two high-density spots connected by a narrow corridor. When pMLF1 passes the corridor, a salt-bridge relay (switching) related to the phosphorylated residue of pMLF1 occurs. Conformations in one high-density spots are similar to the experimentally determined complex structure. Three-dimensional distributions of residues in the intermolecular interface rationally explain the binding-constant changes resultant from alanine–mutation experiment for the residues. We performed a simulation of non-phosphorylated peptide and 14-3-3ε, which demonstrated that the complex structure was unstable, suggesting that phosphorylation of the peptide is crucially important for binding to 14-3-3ε.

We introduced a conformational sampling method in an earlier report: The multi-dimensional virtual-system coupled molecular dynamics (mD-VcMD) enhances conformational sampling of a biomolecular system by computer simulations. Herein, new sampling method, a subzone-based mD-VcMD, is presented as an extension of mD-VcMD. Then, the subzone-based method is extended further using a genetic algorithm (GA) named the GA-guided mD-VcMD. In these methods, iterative simulation runs are performed to increase the sampled region gradually. The new methods have the following benefits: (1) They are free from a production run: i.e., all snapshots from all iterations are useful for analyses. (2) They are free from fine tuning of a weight function (probability distribution function or potential of mean force). (3) A canonical ensemble (i.e., a thermally equilibrated ensemble) is generated from a simple procedure. A thermodynamic weight is assigned to each snapshot. (4) Selective sampling can be performed for particularly addressing a poorly sampled region without breaking the proportion of the canonical ensemble if the poorly sampled conformational region emerges in sampling. By applying the methods to a simple system that involves an energy barrier between potential-energy minima, we demonstrated that the new methods have considerably higher sampling efficiency than the original mD-VcMD does.

Intrinsically disordered regions (IDRs) of a protein employ a flexible binding manner when recognizing a partner molecule. Moreover, it is recognized that binding of IDRs to a partner molecule is accompanied by folding, with a variety of bound conformations often being allowed in formation of the complex. In this study, we investigated a fragment of the disordered p53 C-terminal domain (CTDf) that interacts with one of its partner molecules, S100B, as a representative IDR. Although the 3D structure of CTDf in complex with S100B has been previously reported, the specific interactions remained controversial. To clarify these interactions, we performed generalized ensemble molecular dynamics (MD) simulations (virtual-system coupled multicanonical MD, termed V-McMD), which enable effective conformational sampling beyond that provided by conventional MD. These simulations generated a multimodal structural distribution for our system including CTDf and S100B, indicating that CTDf forms a variety of complex structures upon binding to S100B. We confirmed that our results are consistent with chemical shift perturbations and nuclear Overhauser effects that were observed in previous studies. Furthermore, we calculated the conformational entropy of CTDf in bound and isolated (free) states. Comparison of these CTDf entropies indicated that the disordered CTDf shows further increase in conformational diversity upon binding to S100B. Such entropy gain by binding may comprise an important feature of complex formation for IDRs.