Chia-En A. Chang

Site-directed spin labeling coupled with electron paramagnetic resonance (EPR) is used to characterize structural features in amyloid fibrils, but whether the nitroxide spin label R1 perturbs the structure it reports remains unclear. Molecular dynamics simulations compared wild-type Aβ42 with ten R1-labeled variants in a five-layer protofilament derived from the type I human-brain fibril structure. We quantified nitroxide proximity, regional interlayer packing, backbone hydrogen bonding, R1–protein contacts, and R1 side-chain rotamer sampling. Simulated nitroxide contacts reproduced the experimental distinction between sites with strong and weak spin–spin interactions, although some sites deviated from the trend. Regional interlayer organization and backbone hydrogen bonding were preserved relative to wild-type. However, structural responses were site-dependent and sometimes extended beyond the labeling site. The 32R1 system produced the largest rearrangement of the C-terminal tail. Comparison with the corresponding unlabeled cysteine control showed smaller perturbation, indicating that the response at position 32 arose primarily from the R1 label rather than the cysteine substitution itself. Thus, R1 generally preserved the protofilament architecture and reflected the local packing environment while producing label-specific structural adjustments. These results support R1 as a reporter of local packing in Aβ42 fibrils and demonstrate the value of site-resolved comparisons for identifying label-induced effects.

Molecular conformations play a critical role in determining molecular properties, such as membrane permeability, binding affinity, and ultimately therapeutic efficacy. Experimental and computational approaches can characterize conformations and provide insight into why certain conformations are thermodynamically preferred over others. However, examining static conformations alone may not fully explain why subtle differences, such as a single LEU-to-ILE mutation in a bicyclic peptide, can produce markedly distinct conformational ensembles. Furthermore, analyzing transition pathways between conformations reveals the mechanisms that shape these ensembles. Here, we introduce a deep learning model, termed ICoN-v1, trained on molecular dynamics (MD) simulation data to learn the underlying physics that governs cyclic peptide conformational dynamics. We examined cyclic hexapeptides with Nuclear Magnetic Resonance (NMR)-determined structures, and MYC-targeting bicyclic peptides that are stereo-diversified or have a single LEU-to-ILE mutation. By following minimum-energy pathways in the latent space constructed by ICoN-v1, we efficiently generated smooth conformational transition paths. These pathways reveal sequential sets of concerted backbone and side-chain torsional rotations moving between energy minima. Notably, smooth transition pathways that are absent from MD output were observed using ICoN-v1. Our results identify various sets of concerted torsional motions that are nonlinearly combined during conformational transitions and reveal the key residues governing each stage of the transition, thereby elucidating how the observed conformations are generated and informing molecular design.

Molecular flexibility governs how molecules behave, reorganize, and respond to their environment. Although experiments measure molar entropy for small molecules and molecular dynamics (MD) simulations capture molecular motions, quantifying configuration entropy and the concerted internal motions such as torsion rotations, angle bending, and their couplings are central to understanding thermodynamic behavior but remains challenging. To dissect these contributions, we used MD trajectories and developed an internal coordinate PC-entropy (iPC-entropy) method to probe the origins of entropy and reveal how specific motions shape the thermodynamic landscape. The studies accurately captured molar entropy, identified key torsional motions as major contributors, and uncovered a critical angle-torsion coupling in which angle bending was strongly correlated with torsional rotation, a coupling that increases nonlinearly with molecular size. Evaluating entropic changes upon protein-ligand binding reveals that dominant entropic penalty arises from ligand dihedral rigidification rather than protein reorganization and highlights the specific dihedral rotations that become restricted. We also suggest systematic corrections for approaches considering solely rotamers to reliably reproduce the relative entropic penalty in computer-aided drug discovery. Together, our findings elucidate the molecular origins of entropy and entropy changes. In addition, we can quantify and illustrate the internal motions that strongly shape binding thermodynamics, thereby offering mechanistic insights to guide drug development.

Proteins function through coordinated, time-dependent conformational motions, yet conventional analyses often obscure how these dynamics propagate across complex assemblies. Here, we introduce a generalized Time-Dependent Torsion Angle Correlation (TDTAC) framework that incorporates explicit time lags between residues, enabling quantitative mapping of directional, sequential correlations. By analyzing dihedral rotations, TDTAC reveals conformational dynamics through a physically meaningful representation of residue-level fluctuations. We apply this framework to examine how a small-molecule degrader may induce time-lagged structural rearrangements that facilitate targeted protein degradation. We analyzed the PROteolysis TArgeting Chimera (PROTAC) dBET70, a bifunctional small molecule in complex with its target protein, bromodomain-containing protein 4 (BRD4 ^BD1 ), and assembled within the full degradation complex Cullin-RING Ligase 4A (CRL4A) E3 ligase scaffold. The full assembly comprises nine components: dBET70, BRD4 ^BD1 , and the seven-protein degradation complex CRBN, DDB1, CUL4A, NEDD8, RBX1, E2, and Ub. TDTAC analysis reveals that motions originating at the DDB1–CUL4A region propagate along two dominant pathways: one extending through DDB1 toward the CRBN–BRD4 interface, and the second extending through CUL4A toward the RBX1–Ub interface. These coordinated, time-delayed rearrangements are associated with configurations that bring BRD4, E2, Ub, and the PROTAC into a ubiquitination-competent state. Both pathways exhibit similar lag-dependent propagation behavior, consistent with a network of time-delayed residue-residue coordination linking local torsional dynamics to distal catalytic interfaces. More broadly, TDTAC provides a generalizable framework for resolving dynamic information flow in large biomolecular assemblies.

Polycationic imidazolium macrocycles show strong affinity for anionic and hydrophobic peptides, and can be paired with dyes in an arrayed format to enable site- and state-selective optical detection of serine phosphorylation.

Drug promiscuity is a double-edged sword where a small molecule acts on multiple biological targets to induce toxicological or therapeutic benefits. It is possible to exploit promiscuity to expand treatment options without the prohibitive costs of designing a new drug. Imatinib is a representative case, exhibiting varied affinities and inhibitions to different kinases. It binds most favorably to Abl and Kit kinases, intermediately to Chk1 and Lck kinases, and least favorably to p38α and Src kinases. The strongly conserved features of the ATP-binding site render imatinib’s molecular binding determinants unclear despite over 25 years of interrogation. To address this question, molecular thermodynamics, force distribution analysis, residue side chain dihedral correlations, and principal component analysis were computed using trajectories from all-atom molecular dynamics simulations in an explicit solvent. The results of these simulations agree with experimental affinity and binding data, enabling highly predictive factors for imatinib’s binding specificity from free- and bound-state simulations through a global protein network of protein–ligand interactions, changes in side chain dihedral correlations, and shifts in the secondary motifs modulating binding site access corresponding with well-characterized kinase “breathing motions.” The side chain dihedral correlation network also identifies distal mutants known to reduce patients’ imatinib sensitivity. Higher imatinib-kinase affinity trends with a loss in side chain dihedral correlations and diminished secondary motif migration following binding, corresponding to more restricted configurations, to reduce solvent approach and ATP competition. Lower-affinity proteins show enhanced side chain dihedral correlation and exaggerated secondary motif motions. This is consistent with a tendency to expose the protein pocket, facilitate solvent entrance, and increase ATP competition. Using imatinib as a model system, this study shows residue correlation, force interaction, and essential principal components can effectively forecast imatinib-kinase binding specificity and introduces an effective approach to repurpose and design high-affinity binders for off-target applications more generally.

We propose the integral-equation formalism of population dynamics (IEPDYN) to describe the population dynamics of distinct configurational states. According to classical reaction dynamics theory, the probability density associated with a given state obeys the Liouville equation, including influx from and efflux to neighboring states. By introducing a Markov approximation for the crossing of boundaries separating the states, tractable integral equations governing the state populations are derived. Once the time-dependent quantities appearing in these equations are evaluated, the population dynamics on long timescales can be obtained. Because these quantities depend only on a few states in the local neighborhood of a given state, they can be computed using a set of short-timescale molecular dynamics (MD) simulations. The IEPDYN method is formulated in continuous time and therefore does not rely on a coarse-grained timescale (lag time). Consequently, kinetic quantities obtained from IEPDYN are free from lag-time dependence, which has been discussed as a limitation in other approaches. We apply the IEPDYN method to the binding and unbinding kinetics of CH4/CH4, Na+/Cl−, and 18-crown-6-ether (crown ether)/K+ in water. For both kinetics, the time constants estimated from the IEPDYN method are comparable to those obtained from brute-force MD simulations. The required timescale of each MD trajectory in the IEPDYN method is approximately two orders of magnitude shorter than that in the brute-force MD approach in the crown ether/K+ system. This reduction in the trajectory timescale enables applications to complex binding and unbinding systems whose characteristic timescales are far beyond those directly accessible by brute-force MD simulations.

Molecular conformations play a critical role in determining molecular properties, such as membrane permeability, binding affinity, and ultimately therapeutic efficacy. Experimental and computational approaches can characterize conformations and provide insight into why certain conformations are thermodynamically preferred over others. However, examining conformations alone may not fully explain why subtle differences, such as a single LEU-to-ILE mutation in a bicyclic peptide, can produce markedly distinct conformational ensembles. Analyzing the transition pathways between conformations further reveals the mechanisms that shape these ensembles. Here, we introduce a deep learning model, termed ICoN-v1, trained in molecular dynamics simulation data to learn the underlying physics that governs cyclic peptide conformational dynamics. We examined hexacyclic peptides with Nuclear magnetic resonance (NMR)-determined structures, and MYC-targeting bicyclic peptides, which are stereo-diversified or have a single LEU-to-ILE mutation. By following the minimum-energy pathway in the latent space constructed by ICoN-v1, conformational transition paths, led by various sets of concerted backbone and sidechain torsional rotations moving in sequence between energy minima, are efficiently generated. Notably, smooth transition pathways that are absent from molecular dynamic output can be observed using ICoN-v1. Our results identify various sets of concerted torsional motions that are nonlinearly combined during conformational transitions and reveal the key residues governing each stage of the transition, thereby elucidating how the observed conformations are generated and informing molecular design.

DNA methyltransferase DNMT3A-mediated DNA methylation is important for genomic imprinting and transcriptional regulation. However, how the regulatory domains of DNMT3A cooperate with its methyltransferase domain and histone marks to orchestrate genomic methylation remains unclear. Here we report the cryo-EM structure of DNMT3A2 with regulatory factor DNMT3L, revealing an intricate domain interaction underlying multilayered autoinhibition. The PWWP domain interacts with the ADD and methyltransferase domains to block the target recognition domain and the H3K36me2-binding pocket, thereby coupling the H3K36me2 binding with DNMT3A activation, adding a layer of allosteric regulation distinct from the previously characterized ADD-H3K4me0 regulation. Molecular dynamics simulations of the DNMT3A-DNMT3L complex further reveals that relief of DNMT3A autoinhibition involves disengagement of the CpG-recognition loop of the target recognition domain from autoinhibitory interaction, leading to enhanced accessibility of the target recognition domain loop for DNA binding and DNMT3A activation. Importantly, our combined structural, biochemical and genomic methylation analysis demonstrates that disrupting the PWWP-ADD interaction by disease-associated DNMT3A mutations leads to impaired DNMT3A autoinhibition and substrate specificity, providing a potential explanation to aberrant DNA methylation in disease.