Biology subjects

Chang, C.-e.

Publications and source records attributed to Chang, C.-e..

12 recordsLinked to original sources

bioRxiv2026

Mechanistic Dissection of Entropic Penalty upon Ligand Binding and Molecular Flexibility via Molecular Dynamics Simulations and Machine Learning

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.

biophysics↗

bioRxiv2026

CACNA1C as a Prognostic Biomarker and Therapeutic Target in High-Grade Serous Ovarian Cancer: Clinical Validation and Molecular Dynamics of Nifedipine Blockade

High-Grade Serous Ovarian Cancer (HGSOC) is the most lethal gynecological malignancy due to aggressive growth, widespread metastases, and high intra-tumoral heterogeneity. Poor prognosis is largely due to late diagnosis, hence there is an urgent need to identify novel biomarkers for screening, diagnosis, and monitoring. Here, we propose the voltage-dependent calcium channel hCaV1.2 encoded by CACNA1C as a potential biomarker and therapeutic target in HGSOC. Using IHC analysis for ten ovarian cancer patients, cytotoxicity assay, TCGA gene expression and survival analyses, homology modeling, molecular docking, Calcium channel membrane assembly and molecular dynamics simulations, we tested CACNA1Cs role in HGSOC progression and the effect of blocking on cancer cell survival. We show that nifedipine (NIFE), a calcium channel blocker (CCB), had a tumor suppressive effect based on binding models predicted by three-dimensional computer assisted molecular modeling and in vitro validation using human HGSOC cell line. Using The Cancer Genome Atlas ovarian public cohort, we found CACNA1C mRNA expression strongly correlated with poor patient survival for late-stage and metastasis than primary. We also show strong correlation of CACNA1C protein expression using immunohistochemistry correlating with COH ovarian carcinomas patients disease progression. This research demonstrates that targeting HGSOC via CCBs may be therapeutically beneficial. By establishing further in vitro, in vivo, and clinical trials using FDA approved NIFE may be repurposed to target CACNA1C for HGSOC. Novelty and ImpactHigh-grade serous ovarian cancer (HGSOC) remains lethal due to late diagnosis and drug resistance. This study identifies CACNA1C (Cav1.2) as a novel prognostic biomarker and therapeutic target in HGSOC, showing that elevated expression correlates with metastatic/recurrent disease and poor survival. Using molecular dynamics and in vitro models, we demonstrate that the FDA-approved calcium channel blocker nifedipine binds stably to Cav1.2 and suppresses tumor cell growth more effectively than cisplatin. These findings support repurposing nifedipine for biomarker-driven HGSOC therapy. Translational RelevanceLate diagnosis and progressive relapses significantly contribute to the poor prognosis of ovarian cancer. Identification of a tumor biomarker that can be used for screening, diagnosis, and monitoring is critical for improving clinical outcome. Our findings demonstrate that CACNA1C is a viable diagnostic marker for HGSOC and that its blockade with CCBs reduces tumor progression, highlighting their therapeutic potential.

cancer biology↗

bioRxiv2026

Understanding Conformational Transition of Macrocyclic Peptides through Deep Learning

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.

biophysics↗

bioRxiv2026

Revealing imatinib-kinase specificity via analyzing changes in protein dynamics and computing molecular binding affinity

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 p38 and Src kinases. The strongly conserved features of the ATP-binding site render imatinibs molecular binding determinants unclear despite over 25 years of interrogation. To address this question, molecular thermodynamics, force distribution analysis, residue sidechain dihedral correlations, and principal component analysis were computed using trajectories from all-atom molecular dynamics simulations in explicit solvent. The results of these simulations agree with experimental affinity and binding data, enabling highly predictive factors for imatinibs binding specificity from free- and bound-state simulations through a global protein network of protein-ligand interactions, changes in sidechain dihedral correlations, and shifts in the secondary motifs modulating binding site access corresponding with well-characterized kinase "breathing motions." The sidechain dihedral correlation network also identifies distal mutants known to reduce patients imatinib sensitivity. Higher imatinib-kinase affinity trends with a loss in sidechain dihedral correlations and diminished secondary motif migration following binding, corresponding with more restricted configurations, to reduce solvent approach and ATP competition. Lower-affinity proteins show enhanced sidechain 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.

biochemistry↗

bioRxiv2024

Pathway Specific Unbinding Free Energy Profiles of Ritonavir Dissociation From HIV-1 Protease

Investigation of protein-drug recognition is key in understanding drug selectivity and binding affinity. In combination, binding/unbinding free energy landscape and intermolecular interactions can be used to understand the drug binding/unbinding mechanisms. This information is vital for the development of drugs with improved efficacy and explanation of mutation effects. This study investigated the dissociation processes of ritonavir unbinding from HIV protease (HIVp). Analyzing unbinding trajectories modeled by accelerated molecular dynamics (MD) simulations, three distinct pathways, Pathways A, B, and C, were characterized. Using reduced dimensionality strategy with the principal component analysis, we carried out short classical MD runs with explicit water to sample local fluctuation during ritonavir dissociation and applied the milestoning theory to construct unbinding free energy landscape. We found that each pathway showed similar values of binding free energy, albeit Pathway A accounts for over 50 percent of dissociation trajectories. Interesting, residue-residue correlation network analysis showed that in Pathway A, a broad correlation network outside the flap region governs protein motions during ritonavir unbinding which includes residues with reported mutation effects. However, the other two pathways showed limited correlation networks where no reported mutated residues were involved, explaining the favorability of Pathway A. Guided by the free energy profile, we investigated how and why of an energy barrier and minimum and demonstrated that hydrogen bonding governed the movement of the flap regions, directly impacting the calculated energy. Our study provided a new strategy to estimate ligand binding free energy and demonstrated the importance of the transient interactions during ligand-protein dissociation pathways in understanding drug unbinding.

biochemistry↗

bioRxiv2023

Engineering Nanomolar Potent Protein-based Inhibitors for Papain-like Protease Guided by Residue Correlation Network

Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders is challenging. In addition to direct contacts in a protein-protein binding interface, our study employs computation modeling to reveal the essential network of residue interaction and dihedral angle correlation critical in protein-protein recognition. We propose that mutating residues regions exhibited highly correlated motions within the interaction network can efficiently optimize protein-protein interactions to create tight and selective protein binders. We validated our strategy using ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complexes, where Ub is one central player in many cellular functions and PLpro is an antiviral drug target. Our designed UbV with 3 mutated residues resulted in a [~]3,500-fold increase in functional inhibition, compared with the wild-type Ub. Further optimization by incorporating 2 more residues within the network, the 5-point mutant achieved a KD of 1.5 nM and IC50 of 9.7 nM. The modification led to a 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interaction, introduces an effective approach to design high affinity protein binders for cell biology and future therapeutics solutions.

biophysics↗

bioRxiv2018

Discovery of New CDK8 ligands with a Novel Virtual Drug Screening Tool

Selective inhibition of CDK8 could be a promising strategy for reducing mitogenic signals in cancer cells with reduced toxic effects on normal cells. As compared with type I ligands, binding of a type II compound often achieves longer residence time. We developed a novel virtual drug screening package which takes advantage of two energy evaluation methods: Superposition and Single-Point Energy Evaluation, and VM2 free energy calculation, and applied it to the discovery of new CDK8 type II ligands. In this research we analyzed binding thermodynamics of 11 published CDK8 type II ligands, and extracted the key binding information to assist virtual drug screening for new ligands. The free energy and MD calculations on the reference CDK8-ligand complexes revealed the important factors in the binding. The urea moiety was found to be the critical structural contributor of the reference ligands. Starting with the urea moiety we implemented virtual drug screening and singled out three compounds for bio-assay testing. The ranking from the experimental result for the three compounds is completely consistent with the predicted rankings by both energy evaluation methods. A potent drug-like compound was discovered to have a Kd value of 42.5 nM with CDK8, which is comparable to the most potent reference ligands and provided a good starting point to design and synthesize a series of highly selective and potent CDK8 ligands. Therefore, our novel virtual drug screening package is accurate and efficient enough to be used in drug design projects. We believe this work has significant impact to the field of drug discovery.

bioinformatics↗

bioRxiv2017

Evaluating the accuracy of the umbrella sampling plots with different dissociation paths, conformational changes, and structure preparation

The kinetics of ligand dissociation has been found to be crucial for a good drug candidate. Therefore, examining the underlying free energy profile of the dissociation that governs the kinetics becomes important. Umbrella sampling (US), a widely used free energy calculation method, has long been used to explore the dissociation process of ligand-receptor systems. The potential of mean force (PMF) computed from US seems to always produce binding affinity and energy barriers that more or less agree with experiments. However, such PMFs are influenced by many practical aspects, like the method used to generate the initial dissociation pathway, collective variables (CVs) that used to describe the reaction coordinate (RC), and how intensive the sampling is in the conformational space restrained by the CVs. These critical factors were rarely studied. Here we applied US to study the dissociation processes of {beta}-cyclodextrin ({beta}-CD) and p38 complex systems. For {beta}-CD, we used three different {beta}-CD conformations to generate the dissociation path manually. For p38, we generated the dissociation pathway using accelerated molecular dynamics (AMD) followed by conformational relaxing with short conventional molecular dynamics (MD), steered molecular dynamics (SMD) and manual pulling. We found that even for small {beta}-CD complexes, different {beta}-CD conformations will alter the height of the PMF and different dissociation directions result in appearance/disappearance of local minima. SMD poorly samples the residue sidechain movement, leading to overestimated height of PMF. On the other hand, the AMD pathway relaxed by short conventional MD sampled more accurate structures, resulting in reasonable PMF.

biophysics↗

bioRxiv2017

A QM/MM Study of Biomimetic Catalysis of Diels-Adler reactions Using Cyclodextrins

We performed computational research to investigate the mechanism by which cyclodextrins (CDs) catalyze Diels-Alder reactions between 9-anthracenemethanol and N-cyclohexylmaleimide. Hydrogen bonds (Hbonds) between N-cyclohexylmaleimide and the hydroxyl groups of cyclodextrins were suggested to play an important role in the catalysis.However, our free energy calculations and molecular dynamics simulations showed that these Hbonds are not stable, and quantum mechanics calculations suggested that the reaction is not promoted by these Hbonds. The binding of 9-anthracenemethanol and N-cyclohexylmaleimide to cyclodextrins was the key to the catalysis. Cyclodextrins act as a container to hold the two reactants in the cavity, pre-organizes them for the reactions, and thus reduces the entropy penalty to the activation free energy. Dimethyl-{beta}-CD was a better catalyst for this specific reaction than {beta}-CD because of its stronger van der Waals interaction with the pre-organized reactants and better performance in reducing the activation energy. This computational work sheds light on the mechanism of the catalytic reaction by cyclodextrins and introduces new perspectives of supramolecular catalysis.

biophysics↗

bioRxiv2017

A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery

The activities of CDK8 with partner Cyclin C (CycC) are a common feature of many diseases, especially cancers. Here we report the study of dynamic behaviors and energy profiles of 13 CDK8/CycC systems, including the DMG-in and DMG-out conformations as well as 5 type I ligands and 5 type II ligands, with all-atom unbiased molecular dynamics (MD) simulations. We observed numerous regional motions within CDK8, which move in concert to form five major protein motions. The motion of the activation loop doesnt appear to influence the binding of both types of ligands. Type I ligands remarkably reduce the motion of the C-terminal tail through the strong cation-{pi} interaction between the ligands and ARG356, and type II ligands stabilize the C helix by forming stable hydrogen bonds with GLU66. The MD calculations also confirmed the importance of CycC to the stability of the CDK8 system as well as the ligand binding. The MMPB/SA results show that van der Waals interaction is the main driving force for the binding of both types of ligands, but electrostatic energy and entropy penalty plays important roles in the binding of type II ligands. The volume analysis results indicate that the induced fitting theory applies in the binding of type I ligands. These results would help to improve the affinities of the existing ligands. Our MD work is complementary to crystal structures and may have implications in the development of new CDK8 inhibitors as well as in the field of drug discovery.

biophysics↗

bioRxiv2017

Energy Barriers, Molecular Motions, and Residence Time in Ligand Dissociation: A Computational Study on Type II Inhibitors Binding to CDK8/CycC

This study applies a novel computational strategy to investigate molecular recognition and binding kinetics using five pyrazolourea ligands dissociating from cyclin-dependent kinase 8 with cyclin C (CDK8/CycC) as an example. The computed free energy barriers guide designing compounds using the transient conformations unavailable in experiments. The intermediates and their free energy profile during ligand association and discussion processes control ligand-protein binding kinetics and bring a more complete picture of ligand-protein binding. We used metadynamics and a pathway search method to sample pathways and applied combined reduced dimensionality, molecular dynamics (MD) simulations and milestoning theory to construct the free energy profile and estimate the residence time. The binding free energy and the trend of binding kinetics agreed with experiments. We explain the why of the barriers and the kinetics and use the information to assist ligand design. Guided by a barrier of a ligand passing an C helix and activation loop, we introduced one hydroxyl group to parent compounds to design our ligands with increased residence time and validated our prediction by experiments. This work provides a novel and robust approach to investigate dissociation kinetics of large and flexible systems for understanding unbinding mechanisms and designing new small molecule drugs with desired binding kinetics.\n\nSignificance StatementThe transient conformations during ligand binding/unbinding control non-covalent binding kinetics. However, the transient structures and their free energy landscape of flexible ligand-protein systems are unavailable in experiments and challenging to model. Due to lack of understanding in binding kinetics, even scientists know that kinetic properties can be important in drug development, calculations using the intermediate states to design ligands with preferred binding kinetics are absent. We overcome these challenges and compute ligand-protein unbinding free energy profile using a novel method with molecular dynamics simulations, reduced dimensionality, and milestoning theory to deepen our understanding in molecular recognition. We also designed compounds based on the computed free energy barriers and experimentally validated that our designed compound can increase residence time.

biophysics↗

bioRxiv2017

Binding Thermodynamics and Kinetics Calculations Using Chemical Host and Guest: A Comprehensive Picture of Molecular Recognition

Understanding the fine balance between changes of entropy and enthalpy and the competition between a guest and water molecules in molecular binding is crucial in fundamental studies and practical applications. Experiments provide measurements. However, illustrating the binding/unbinding processes gives a complete picture of molecular recognition not directly available from experiments, and computational methods bridge the gaps. Here, we investigated guest association/dissociation with {beta}-cyclodextrin ({beta}-CD) by using microsecond-timescale molecular dynamics (MD) simulations, post-analysis and numerical calculations. We computed association and dissociation rate constants, enthalpy, and solvent and solute entropy of binding. All the computed values of kon, koff, {Delta}H, {Delta}S, and {Delta}G using GAFF-CD and q4MD-CD force fields for {beta}-CD could be compared with experimental data directly and agreed reasonably with experiment findings. Both force fields resulted in similar computed {Delta}G from independently computed kinetics rates, {Delta}G=-RTln(kon {middle dot} C{degrees} / k off), and thermodynamics properties, {Delta}G={Delta}H - T{Delta}S. The water entropy calculations show that entropy gain of desolvating water molecules are a major driving force, and both force fields have the same strength of non-polar attractions between solutes and {beta}-CD as well. Water molecules play a crucial role in guest binding to {beta}-CD. However, collective water/{beta}-CD motions could contribute to different computed kon and {Delta}H values by different force fields, mainly because the parameters of {beta}-CD provide different motions of {beta}-CD, hydrogen-bond networks of water molecules in the cavity of free {beta}-CD and the strength of desolvation penalty. As a result, q4MD-CD suggests that guest binding is mostly driven by enthalpy, while GAFF-CD shows that gaining entropy is the major driven force of binding. The study further interprets experiments, deepens our understanding of ligand binding, and suggests strategies for force field parameterization.

biophysics↗