Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Shotgun lipidomics of human subretinal fluids under rod-dominant retina reveals cone-dominated lipids.

Subretinal fluid (SRF) accumulates between photoreceptor outer segments and retinal pigment epithelium during rhegmatogenous retinal detachment. Biomolecular components such as lipids originate from cells surrounding the SRF. Knowledge of the composition of these molecules in SRF potentially provides mechanistic insight into the physiologic transfer of lipids between retinal tissue compartments. Using mass spectrometry and tandem mass spectrometry analysis on an electrospray ionization quadrupole-time-of-flight mass spectrometer, we identified a total of 115 lipid molecular species of 11 subclasses and 9 classes in two samples from two patients with rhegmatogenous retinal detachment. These included 47 glycerophosphocholines, 6 glycerophosphoethanolamines, 1 glycerophosphoinositol, 18 sphingomyelins, 9 cholesteryl esters, free cholesterol, 3 ceramides, 22 triacylglycerols and 8 free fatty acids. Glycerophosphocholines were of the highest intensity. By minimizing the formation of different adduct forms or clustering ions of different adducts, we determined the relative intensity of lipid molecular species within the same subclasses. The profiles were compared with those of retinal cells available in the published literature. The glycerophosphocholine profile of SRF was similar to that of cone outer segments, suggesting that outer segment degradation products are constitutively released into the interphotoreceptor matrix, appearing in SRF during detachment. This hypothesis was supported by the retinal distributions of corresponding lipid synthases' mRNA expression obtained from an online resource based on publicly available single-cell sequencing data. In contrast, based on lipid profiles and relevant gene expression in this study, the sources of free cholesterol and cholesteryl esters in SRF appeared more ambiguous, possibly reflecting that outer retina takes up plasma lipoproteins. Further studies to identify and quantify lipids in SRF will help better understand etiology of diseases relevant to outer retina.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.

The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were combined with deep learning (DL) to explore the molecular mechanism of the phosphorylation-mediated effect on conformational dynamics of the GTP-bound KRAS. The DL finds that the switch domains are involved in obvious differences in conformation contacts and suggests that the switch domains play a key role in the function of KRAS. The analyses of free energy landscapes (FELs) reveal that the phosphorylation of pY32, pY64, and pY137 leads to more disordered states of the switch domains than the wild-type (WT) KRAS and induces conformational transformations between the closed and open states. The results from principal component analysis (PCA) indicate that principal motions PC1 and PC2 are responsible for the closed and open states of the phosphorylated KRAS. Interaction networks were analyzed and the results verify that the phosphorylation alters interactions of GTP and magnesium ion Mg2+ with the switch domains. It is concluded that the phosphorylation pY32, pY64, and pY137 tune the activity of KRAS through changing conformational dynamics and interactions of the switch domains. We anticipated that this work could provide theoretical aids for deeply understanding the function of KRAS.

Binding Mechanism of Inhibitors to BRD4 and BRD9 Decoded by Multiple Independent Molecular Dynamics Simulations and Deep Learning.

Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calculations are integrated to probe the binding modes of three inhibitors (H1B, JQ1 and TVU) to BRD4 and BRD9. The MD trajectory-based DL successfully identify significant functional function domains, such as BC-loop and ZA-loop. The information from the post-processing analysis of MD simulations indicates that inhibitor binding highly influences the structural flexibility and dynamic behavior of BRD4 and BRD9. The results of the MM-GBSA calculations not only suggest that the binding ability of H1B, JQ1 and TVU to BRD9 are stronger than to BRD4, but they also verify that van der Walls interactions are the primary forces responsible for inhibitor binding. The hot spots of BRD4 and BRD9 revealed by residue-based free energy estimation provide target sites of drug design in regard to BRD4 and BRD9. This work is anticipated to provide useful theoretical aids for the development of selective inhibitors over BRD family members.

Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study.

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity.

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

Synthesis of 2,3-Fused Quinazolinones via the Radical Cascade Pathway and Reaction Mechanistic Studies by DFT Calculations.

An efficient radical cascade cyclization of unactivated alkenes toward the synthesis of a series of ring-fused quinazolinones has been developed in moderate to excellent yields using commercially available ethers, alkanes, and alcohols, respectively, under a base-free condition in a short time without a transition metal as catalyst. Notably, the transformations can be carried out with the advantages of a broad substrate scope and high atomic economy. Density functional theory calculations and wavefunction analyses were performed to elucidate the radical reaction mechanism.

Molecular insights and optimization strategies for the competitive binding of engineered ACE2 proteins: a multiple replica molecular dynamics study.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally, and rapid viral evolution and the emergence of new variants pose challenges to pandemic control. During infection, the spike protein of SARS-CoV-2 interacts with the human ACE2 protein via its receptor binding domain (RBD), and it is known that engineered forms of ACE2 can compete with wild-type (WT) ACE2 for binding to inhibit infection. Here, we conducted multiple replica molecular dynamics (MRMD) simulations to study the mechanisms of the engineered ACE2 variants 3N39 and 3N94 and provide directions for optimization. Our findings reveal that engineered ACE2 is notably more efficacious in systems that show weaker binding to WT ACE2 (i.e., WT and BA.1 RBD), but also faces immune escape as the virus evolves. Moreover, by modifying residue types near the binding interface, engineered ACE2 alters the electrostatic potential distribution and reconfigures the hydrogen bonding network, which results in modified binding to the RBD. However, this structural rearrangement does not occur in all RBD variants. In addition, we identified potentially engineerable beneficial residues and potentially engineerable detrimental residues in both ACE2 and RBD. Functional conservation can thus enable the optimization of these residues and improve the binding competitiveness of engineered ACE2, which therefore provides additional immune escape prevention. Finally, we conclude that these findings have implications for understanding the mechanisms responsible for engineered ACE2 and can help us to develop engineered ACE2 proteins that show superior performance.

Probing mutation-induced conformational transformation of the GTP/M-RAS complex through Gaussian accelerated molecular dynamics simulations.

Mutations highly affect the structural flexibility of two switch domains in M-RAS considered an important target of anticancer drug design. Gaussian accelerated molecular dynamics (GaMD) simulations were applied to probe the effect of mutations P40D, D41E, and P40D/D41E/L51R on the conformational transition of the switch domains from the GTP-bound M-RAS. The analyses of free energy landscapes (FELs) not only reveal that three mutations induce less energetic states than the wild-type (WT) M-RAS but also verify that the switch domains are extremely disordered. Principal component analysis (PCA) and dynamics analysis suggest that three mutations greatly affect collective motions and structural flexibility of the switch domains that mostly overlap with binding regions of M-RAS to its effectors, which in turn disturbs the activity of M-RAS. The analyses of the interaction network between GTP and M-RAS show that the high instability in hydrogen bonding interactions (HBIs) of GTP with residue 41 and Y42 in the switch domain I drives the disordered states of the switch domains. This work is expected to provide a molecular mechanism for deeply understanding the function of M-RAS and future drug design towards the treatment of cancers.

Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies.

ß-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.

Binding Mechanism of Inhibitors to Heat Shock Protein 90 Investigated by Multiple Independent Molecular Dynamics Simulations and Prediction of Binding Free Energy.

The heat shock protein (HSP90) has been an import target of drug design in the treatment of human disease. An exploration of the conformational changes in HSP90 can provide useful information for the development of efficient inhibitors targeting HSP90. In this work, multiple independent all-atom molecular dynamics (AAMD) simulations followed by calculations of the molecular mechanics generalized Born surface area (MM-GBSA) were performed to explore the binding mechanism of three inhibitors (W8Y, W8V, and W8S) to HSP90. The dynamics analyses verified that the presence of inhibitors impacts the structural flexibility, correlated movements, and dynamics behavior of HSP90. The results of the MM-GBSA calculations suggest that the selection of GB models and empirical parameters has important influences on the predicted results and verify that van der Waals interactions are the main forces that determine inhibitor-HSP90 binding. The contributions of separate residues to the inhibitor-HSP90 binding process indicate that hydrogen-bonding interactions (HBIs) and hydrophobic interactions play important roles in HSP90-inhibitor identifications. Moreover, residues L34, N37, D40, A41, D79, I82, G83, M84, F124, and T171 are recognized as hot spots of inhibitor-HSP90 binding and provide significant target sites of for the design of drugs related to HSP90. This study aims to contribute to the development of efficient inhibitors that target HSP90 by providing an energy-based and theoretical foundation.

Nickel-Catalyzed Asymmetric Hydrogenation of α-Substituted Vinylphosphonates and Diarylvinylphosphine Oxides.

Chiral α-substituted ethylphosphonate and ethylphosphine oxide compounds are widely used in drugs, pesticides, and ligands. However, their catalytic asymmetric synthesis is still rare. Of the only asymmetric hydrogenation methods available at present, all cases use rare metal catalysts. Herein, we report an efficient earth-abundant transition-metal nickel catalyzed asymmetric hydrogenation affording the corresponding chiral ethylphosphine products with up to 99 % yield, 96 % ee (enantiomeric excess) (99 % ee, after recrystallization) and 1000 S/C (substrate/catalyst); this is also the first study on the asymmetric hydrogenation of terminal olefins using a nickel catalyst under a hydrogen atmosphere. The catalytic mechanism was investigated via deuterium-labelling experiments and calculations which indicate that the two added hydrogen atoms of the products come from hydrogen gas. Additionally, it is believed that the reaction involves a NiII rather than Ni0 cyclic process based on the weak attractive interactions between the Ni catalyst and terminal olefin substrate.

Copper-Catalyzed Chemoselective Asymmetric Hydrogenation of C=O Bonds of Exocyclic α,ß-Unsaturated Pentanones.

A highly chemoselective earth-abundant transition metal copper catalyzed asymmetric hydrogenation of C=O bonds of exocyclic α,ß-unsaturated pentanones was realized using H2 . The desired products were obtained with up to 99 % yield and 96 % ee (enantiomeric excess) (99 % ee, after recrystallization). The corresponding chiral exocyclic allylic pentanol products can be converted into several bioactive molecules. The hydrogenation mechanism was investigated via deuterium-labelling experiments and control experiments, which indicate that the keto-enol isomerization rate of the substrate is faster than that of the hydrogenation and also show that the Cu-H complex can only catalyze chemoselectively the asymmetric reduction of the carbonyl group. Computational results indicate that the multiple attractive dispersion interactions (MADI effect) between the catalyst with bulky substituents and substrate play important roles which stabilize the transition states and reduce the generation of by-products.

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development.

Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Goal-specific brain MRI harmonization.

There is significant interest in pooling magnetic resonance image (MRI) data from multiple datasets to enable mega-analysis. Harmonization is typically performed to reduce heterogeneity when pooling MRI data across datasets. Most MRI harmonization algorithms do not explicitly consider downstream application performance during harmonization. However, the choice of downstream application might influence what might be considered as study-specific confounds. Therefore, ignoring downstream applications during harmonization might potentially limit downstream performance. Here we propose a goal-specific harmonization framework that utilizes downstream application performance to regularize the harmonization procedure. Our framework can be integrated with a wide variety of harmonization models based on deep neural networks, such as the recently proposed conditional variational autoencoder (cVAE) harmonization model. Three datasets from three different continents with a total of 2787 participants and 10,085 anatomical T1 scans were used for evaluation. We found that cVAE removed more dataset differences than the widely used ComBat model, but at the expense of removing desirable biological information as measured by downstream prediction of mini mental state examination (MMSE) scores and clinical diagnoses. On the other hand, our goal-specific cVAE (gcVAE) was able to remove as much dataset differences as cVAE, while improving downstream cross-sectional prediction of MMSE scores and clinical diagnoses.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Transthyretin proteoforms of intraocular origin in human subretinal fluid.

Understanding the molecular composition of ocular tissues and fluids could inform new approaches to prevalent causes of blindness. Subretinal fluid accumulating between the photoreceptor outer segments and retinal pigment epithelium (RPE) is potentially a rich source of proteins and lipids normally cycling among outer retinal cells and choroid. Herein, intact post-translationally modified proteins (proteoforms) were extracted from subretinal fluids of five patients with rhegmatogenous retinal detachment (RRD), analyzed by tandem mass spectrometry, and compared to published data on these same proteins as synthesized by other organs. Single-nuclei transcriptomic data from non-diseased human retina/RPE were used to identify whether proteins in subretinal fluid were of potential ocular origin. Two human donor eyes with normal maculas were immunoprobed for transthyretin (TTR) with appropriate controls. The three most abundant proteins detected in subretinal fluid were albumin, TTR, and apolipoprotein A-I. Remarkably, TTR relative to the other proteins was more abundant than its serum counterpart, suggestive of TTR being synthesized predominantly locally. Six proteoforms of TTR were detected, with the relative amount of glutathionylated TTR being much higher in the subretinal fluid (12-43%) than values reported for serum (<5%) and cerebrospinal fluid (0.4-13%). Moreover, a putative glycosylated TTR dimer of 32,428 Da was detected as the fourth most abundant protein. The high abundance of TTR and putative TTR dimer in subretinal fluid was supported by analysis of available single-nuclei transcriptomic data, which showed strong and specific signal for TTR in RPE. Immunohistochemistry further showed strong diffuse TTR immunoreactivity in choroidal stroma that contrasted with vertically aligned signal in the outer segment zone of the subretinal space and negligible signal in RPE cell bodies. These results suggest that TTR in the retina is synthesized intraocularly, and glutathionylation is crucial for its normal function. Further studies on the composition, function, and quantities of TTR and other proteoforms in subretinal fluid could inform mechanisms, diagnostic methods, and treatment strategies for age-related macular degeneration, familial amyloidosis, and other retinal diseases involving dysregulation of physiologic lipid transfer and oxidative stress.

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations.

S-Adenosyl-l-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (SMK) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand-nucleotide interactions not only uncovers that the π-π, cation-π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.