Biology subjects

Singharoy, A.

Publications and source records attributed to Singharoy, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Ultrafast X-Rays Capture Retinal Traversing Conical Intersection in Rhodopsin

G-protein-coupled receptors of the Rhodopsin family are crucial medicinal targets, transmitting signals across biomembranes. While light absorption by visual rhodopsin is well studied, its activation via retinal cofactor dynamics remains unclear. Here we use a free-electron laser to show that time-resolved X-ray solution scattering captures the retinal cis-trans isomerization as it passes the conical intersection of excited and ground-state photoproduct energy surfaces. Femtosecond-scale nuclear changes occur due to resonant photon absorption, with all-atom simulations revealing ultrafast amino acid movements that initiate transmembrane helix shifts. Ligand-free opsin measurements confirm that light activation is unaffected by non-resonant processes, showing the photonic energy is directly transmitted within the protein. Our method unveils how cofactor dynamics activate rhodopsin, free of constraints from crystal lattice packing or cryotrapping photointermediates.

biophysics↗

bioRxiv2025

Prebiotically Plausible Peptides can Self-assemble into β-rich Nanostructures

Peptides can self-assemble into diverse morphologies in a programmable manner and hence are privileged building blocks used widely in nanotechnology. Most reported peptide nanostructures consist of one (or a few) defined sequence(s), as self-similarity is presumed to be essential for promoting assembly. While oligomerisation is seen as an important feature of the earliest functional polymers during the origin of life, prebiotic peptides were likely short, statistical, and non-templated - traits that seem incommensurate with robust self-assembly. Here we show that random 25-mer peptides can efficiently and spontaneously form highly thermostable, soluble assemblies rich with beta-sheets. Notably, these nanostructures only emerge when random peptides are constructed with an early alphabet, consisting of the 10 canonical amino acids that were also prebiotically abundant - but not other alphabets tested. Hence, our findings show that peptide self-assembly does not require purity, and in fact compositional complexity is adaptive for preventing formation of insoluble structures. Altogether, this study showcases that unevolved sequences of prebiotically-abundant amino acids can readily produce foldable self-assembling polymers, thereby providing a potential steppingstone toward the first proteins, prior to the onset of purifying selection.

biochemistry↗

bioRxiv2024

Transient protein structure guides surface diffusion pathways for electron transport in membrane supercomplexes

The biological significance of protein supercomplexes have remained contentious, particularly how they tune the shuttling of charge-carrier redox proteins across cell membranes during biological energy conversion. We employ multiscale modeling and single particle cryo-electron microscopy (cryo-EM) to determine the mechanisms of diffusive electron transfer in mitochondrial supercomplexes, composed of respiratory complexes III and IV (CIII and CIV). Using a combination of bioinformatic and entropy maximization tools, we model an ensemble of structures representing the conformational space of CIIIs disordered QCR6 hinge within the yeast CIII2CIV2 supercomplex. Molecular and Brownian Dynamics simulations of the entire supercomplex reveal a mechanism for electrostatic coupling between these negatively charged hinge conformations, and binding and directional diffusion of the redox proteins on the mitochondrial membrane, which is simulated over the millisecond timescale. Anionic lipids reinforce this conformationally-coupled recognition of the supercomplex by retaining a pool of the redox proteins in the vicinity of the membrane when the hinges are of a critical length. Cryo-EM models reveal a large-scale rearrangement of the {Delta}QCR6 supercomplex, which retains a surprisingly robust electrostatic environment for recognition of the redox protein, despite compromise in the supercomplexs negative charge, still enabling a surface-mediated electron transfer in this CIII2CIV2 variant. Altogether, the evolutionary need of confining electron carriers on the surface of bioenergetic membranes is found to give rise to a refolding-guided diffusion model of the redox proteins, which improves the energy conversion efficiency within the supercomplex by nearly 30%.

biophysics↗

bioRxiv2024

Finding the E-Channel Proton Loading Sites by Calculating the Ensemble of Protonation Microstates

The aerobic electron transfer chain builds a proton gradient by proton coupled electron transfer reactions through multiple proteins. Complex I is the first enzyme in this chain. On transfer of two electrons from NADH to quinone four protons are pumped from the N- (negative, higher pH) to the P- (positive, lower) side. Protons move pthrough three linear antiporter paths, with a few amino acids and waters providing the route and the E-channel, which is a complex of competing paths, with clusters of interconnected protonatable residues. Proton loading sites (PLS) are residues that transiently bind protons as they are transported from N- to P-compartments. PLS can be individual residues or extended clusters. The program MCCE uses Monte Carlos sampling to analyze the E-channel proton binding in equilibrium with individual Molecular Dynamics snapshots from trajectories of Thrmus thermuphillus Complex I in the apo, quinone or quinol bound states. At pH 7, the five E-channel subunits (Nqo4, Nqo7, Nqo8, Nqo10, and Nqo11) have accepted >25,000 protonation microstates, each with different residues protonated. The microstate explosion is tamed by analyzing interconnected clusters of residues along the proton transfer paths. A proton is bound and released to five coupled residues in a cluster on the protein N-side and to six coupled residues in the protein center. Loaded microstates bind protons to sites closer to the P-side in the forward pumping direction. MCCE microstate analysis shows the strong coupling of proton binding amongst individual residues in the two PLS clusters.

biophysics↗

bioRxiv2024

Upregulation of the Proto-Oncogene Src Kinase in Alzheimer's Disease: From Molecular Interactions to Therapeutic Potential

Alzheimers disease (AD) is a progressive neurodegenerative disease, resulting in an irreversible deterioration of multiple brain regions associated with cognitive dysfunction. Phosphorylation of the microtubule-associated protein, Tau, is known to occur decades before symptomatic AD. The Src family of tyrosine kinases are known to phosphorylate select tyrosine sites on Tau and promote microtubule disassembly and subsequent neurofibrillary tangle (NFT) formation. Our data show that the proto-oncogene, non-receptor tyrosine kinase Src colocalizes with a range of late (PHF1) to early (MC1) AD-associated phosphorylated Tau epitopes. The strongest co-occurrence is seen with MC1 (probability of MC1 given Src =100%), an early AD-specific conformational dependent epitope. Single-cell RNA sequencing data of 101 subjects show that Src is upregulated in both AD inhibitory and excitatory neurons. The most significantly affected, by orders of magnitude, were excitatory neurons which are the most prone to pathological Tau accumulation. We measured Src phosphorylation by mass spectrometry across a cohort of 48 patient neocortical tissues and found that Src has increased phosphorylation on Ser75, Tyr187, and Tyr440 in AD, showing that Src kinase undergoes distinct phosphorylation alterations in AD. Through Brownian dynamics simulations of Src and Tau, we show that as Tau undergoes the transition into disease-associated paired helical filaments, there is a notable seven-fold increase in Src contact with Tau. These results collectively emphasize Src kinases central role in Tau phosphorylation and its close association with Tau epitopes, presenting a promising target for potential therapeutic intervention.

neuroscience↗

bioRxiv2023

Structural mechanism of HP1-dependent transcriptional repression and chromatin compaction

Heterochromatin protein 1 (HP1) plays a central role in establishing and maintaining constitutive heterochromatin. However, the mechanisms underlying HP1-nucleosome interactions and their contributions to heterochromatin functions remain elusive. In this study, we employed a multidisciplinary approach to unravel the interactions between human HP1 and nucleosomes. We have elucidated the cryo-EM structure of an HP1 dimer bound to an H2A.Z nucleosome, revealing that the HP1 dimer interfaces with nucleosomes at two distinct sites. The primary binding site is located at the N-terminus of histone H3, specifically at the trimethylated K9 (K9me3) region, while a novel secondary binding site is situated near histone H2B, close to nucleosome superhelical location 4 (SHL4). Our biochemical data further demonstrates that HP1 binding influences the dynamics of DNA on the nucleosome. It promotes DNA unwrapping near the nucleosome entry and exit sites while concurrently restricting DNA accessibility in the vicinity of SHL4. This study offers a model that explains how HP1 functions in heterochromatin maintenance and gene silencing, particularly in the context of H3K9me-dependent mechanisms. Additionally, it sheds light on the H3K9me-independent role of HP1 in responding to DNA damage.

biochemistry↗

bioRxiv2023

Adaptive Artificial Intelligence to Teach Interactive Molecular Dynamics in the Context of Human-Computer Interaction

Artificial Intelligence (AI) can be easily integrated into virtual education to drive adaptive instruction and real-time constructive feedback to students, offering a possible conduit for fostering discovery curiosity in learners. This study examines and characterizes Human-AI-Teaming (HAT) coordination dynamics to monitor the inception of discovery curiosity in online laboratories of interactive molecular dynamics (IMD). We used molecular physics measures (kinetic/ potential energy and action) obtained from simple and complex examples of simulated mouse tracking datasets in IMD log files as a proxy for understanding the context of molecular sciences and developing novel interactions for inquiry. These measures are good features of our HAT context because kinetic energy reflects the systems atoms overall motion regarding the individual atoms speed. While kinetic energy represents if a learner applies artificial forces to the task, potential energy can be AIs response to these forces. The action is a systems-level reaction to the changes during the task. By applying nonlinear dynamical systems methods to the physics measures, we extracted the Largest Lyapunov Exponent and Determinism metrics as HATs coordination stability and predictability, respectively. The findings underline that while the more complex IMD task required less stable and predictable HAT coordination dynamics, the simple task is more. One explanation is that AI needs to anticipate the learner by providing feedback at the right time and place during the more complex IMD task to initiate and sustain the learners discovery curiosity. In IMD, future HAT design should consider coordination dynamics for fostering discovery curiosity and practical learning.

bioengineering↗

bioRxiv2023

Glycolytic Metabolon Assembly on Mitochondria via Hexokinase O-GlcNAcylation Promotes Metabolic Efficiency

Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme Hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, crucial for efficient glycolysis-oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis, and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyzes reversible O-GlcNAcylation, a post-translational modification, influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of HK1s regulatory domain, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances HK1s mitochondrial association, orchestrating glycolytic and mitochondrial ATP production. Mutations in HK1s O-GlcNAcylation site reduce ATP generation, affecting synaptic functions in neurons. The study uncovers a novel pathway that bridges neuronal metabolism and mitochondrial function via OGT and the formation of the glycolytic metabolon, offering new prospects for tackling metabolic and neurological disorders.

cell biology↗

bioRxiv2022

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynskis equality and the stiffspring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.

biophysics↗

bioRxiv2022

High-resolution structures map the metal import pathway in an Nramp transporter

Transporters of the Nramp (Natural resistance-associated macrophage protein) family import divalent transition metal ions into cells of most organisms. By supporting metal homeostasis, Nramps prevent disorders related to metal insufficiency or overload. Previous studies revealed that Nramps take on a LeuT fold and identified the metal-binding site. We present high- resolution structures of Deinococcus radiodurans Nramp in three stable conformations of the transport cycle revealing that global conformational changes are supported by distinct coordination geometries of its physiological substrate, Mn2+, across conformations and conserved networks of polar residues lining the inner and outer gates. A Cd2+-bound structure highlights differences in coordination geometry for Mn2+ and Cd2+. Measurements of metal binding using isothermal titration calorimetry indicate that the thermodynamic landscape for binding and transporting physiological metals like Mn2+ is different and more robust to perturbation than for transporting the toxic Cd2+ metal.

biochemistry↗

bioRxiv2021

Scalable Adaptive Protein Ensemble Refinement Integrating Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. We introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehy-drogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 [A] map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

biophysics↗

bioRxiv2021

Revealing a hidden intermediate of rotatory catalysis with X-ray crystallography and Molecular simulations

The mechanism of rotatory catalysis in ATP-hydrolyzing molecular motors remain an unresolved puzzle in biological energy transfer. Notwith standing the wealth of available biochemical and structural information inferred from years of experiments, knowledge on how the coupling between the chemical and mechanical steps within motors enforces directional rotatory movements remains fragmentary. Even more contentious is to pinpoint the rate-limiting step of a multi-step rotation process. Here, using Vacuolar or V1-type hexameric ATPase as an exemplary rotational motor, we present a model of the complete 4-step conformational cycle involved in rotatory catalysis. First, using X-ray crystallography a new intermediate or dwell is identified, which enables the release of an inorganic phosphate (or Pi) after ATP hydrolysis. Using molecular dynamics simulations, this new dwell is placed in a sequence with three other crystal structures to derive a putative cyclic rotation path. Free-energy simulations are employed to estimate the rate of the hexameric protein transformations, and delineate allosteric effects that allow new reactant ATP entry only after hydrolysis product exit. An analysis of transfer entropy brings to light how the sidechain-level interactions transcend into larger-scale reorganizations, highlighting the role of the ubiquitous arginine-finger residues in coupling chemical and mechanical information. Inspection of all known rates encompassing the 4-step rotation mechanism implicates overcoming of the ADP interactions with V1-ATPase to be the rate-limiting step of motor action.

biophysics↗

bioRxiv2021

Characterization and computational simulation of human Syx, a RhoGEF implicated in glioblastoma

Structural discovery of guanine nucleotide exchange factor (GEF) protein complexes is likely to become increasingly relevant with the development of new therapeutics targeting small GTPases and development of new classes of small molecules that inhibit protein-protein interactions. Syx (also known as PLEKHG5 in humans) is a RhoA GEF implicated in the pathology of glioblastoma (GBM). Here we investigated protein expression and purification of ten different human Syx constructs and performed biophysical characterizations and computational studies that provide insights into why expression of this protein was previously intractable. We show that human Syx can be expressed and isolated and Syx is folded as observed by circular dichroism (CD) spectroscopy and actively binds to RhoA as determined by co-elution during size exclusion chromatography (SEC). This characterization may provide critical insights into the expression and purification of other recalcitrant members of the large class of oncogenic -- Diffuse B-cell lymphoma (Dbl) homology GEF proteins. In addition, we performed detailed homology modeling and molecular dynamics simulations on the surface of a physiologically realistic membrane. These simulations reveal novel insights into GEF activity and allosteric modulation by the plekstrin homology (PH) domain. These newly revealed interactions between the GEF PH domain and the membrane embedded region of RhoA support previously unexplained experimental findings regarding the allosteric effects of the PH domain from numerous activity studies of Dbl homology GEF proteins. This work establishes new hypotheses for structural interactivity and allosteric signal modulation in Dbl homology RhoGEFs.

biochemistry↗

bioRxiv2021

The Structure of ChAdOx1/AZD-1222 Reveals Interactions with CAR and PF4 with Implications for Vaccine-induced Immune Thrombotic Thrombocytopenia

Adenovirus derived vectors, based on chimpanzee adenovirus Y25 (ChAdOx1) and human adenovirus type 26 are proving critical in combatting the 2019 SARS-CoV-2 pandemic. Following emergency use authorisation, scale up in vaccine administration has inevitably revealed vaccine related adverse effects; too rare to observe even in large Phase-III clinical trials. These include vaccine-induced thrombotic thrombocytopenia (VITT), an ultra-rare adverse event in which patients develop life-threatening blood clots 5-24 days following vaccination. To investigate vector-host interactions of ChAdOx1 underpinning VITT we solved the structure of the ChAdOx1 capsid by CryoEM, and the structure of the primary receptor tropism determining fiber-knob protein by crystallography. These structural insights have enabled us to unravel key protein interactions involved in ChAdOx1 cell entry and a possible means by which it may generate misplaced immunity to platelet factor 4 (PF4), a protein involved in coagulation. We use in vitro cell binding assays to show that the fiber-knob protein uses coxsackie and adenovirus receptor (CAR) as a high affinity binding partner, while it does not form a stable interface with CD46. Computational simulations identified a putative mechanism by which the ChAdOx1 capsid interacts with PF4 by binding in the spaces between hexon proteins, with downstream implications for the causes of VITT. SummaryWe present the structure of the ChAdOx1 viral vector, derived from chimpanzee adenovirus Y25 at 4.2[A] resolution1. ChAdOx1 is in global use in the AstraZeneca vaccine, ChAdOx1 nCoV-19/AZD-1222, to combat the SARS-CoV-2 coronavirus pandemic. Recently observed, rare, adverse events make detailed mechanistic understanding of this vector key to informing proper treatment of affected patients and the development of safer viral vectors. Here, we determine a primary mechanism ChAdOx1 uses to attach to cells is coxsackie and adenovirus receptor (CAR), a protein which is identical in humans and chimpanzees. We demonstrate the vector does not form a stable CD46 interaction, a common species B adenovirus receptor, via its primary attachment protein. Further, we reveal the surface of the ChAdOx1 viral capsid has a strong electronegative potential. Molecular simulations suggest this charge, together with shape complementarity, are a mechanism by which an oppositely charged protein, platelet factor 4 (PF4) may bind the vector surface. PF4 is a key protein involved in the formation of blood clots2, and the target of auto-antibodies in heparin-induced immune thrombotic thrombocytopenia (HITT)3, an adverse reaction to heparin therapy which presents similarly to vaccine-induced immune thrombotic thrombocytopenia (VITT), a rare complication of ChAdOx1 nCoV-19 vaccination4-6. We propose a mechanism in which the ChAdOx1-PF4 complex may stimulate the production of antibodies against PF4, leading to delayed blood clot formation, as observed in VITT.

molecular biology↗

bioRxiv2021

Energy Landscape of the SARS-CoV-2 Reveals Extensive Conformational Heterogeneity

Cryo-electron microscopy (cryo-EM) has produced a number of structural models of the SARS-CoV-2 spike, already prompting biomedical outcomes. However, these reported models and their associated electrostatic potential maps represent an unknown admixture of conformations stemming from the underlying energy landscape of the spike protein. As for any protein, some of the spikes conformational motions are expected to be biophysically relevant, but cannot be interpreted only by static models. Using experimental cryo-EM images, we present the energy landscape of the spike protein conformations, and identify molecular rearrangements along the most-likely conformational path in the vicinity of the open (so called 1RBD-up) state. The resulting global and local atomic refinements reveal larger movements than those expected by comparing the reported 1RBD-up and 1RBD-down cryo-EM models. Here we report greater degrees of "openness" in global conformations of the 1RBD-up state, not revealed in the single-model interpretations of the density maps, together with conformations that overlap with the reported models. We discover how the glycan shield contributes to the stability of these conformations along the minimum free-energy pathway. A local analysis of seven key binding pockets reveals that six out them, including those for engaging ACE2, therapeutic mini-proteins, linoleic acid, two different kinds of antibodies, and protein-glycan interaction sites, switch conformations between their known apo- and holo-conformations, even when the global spike conformation is 1RBD-up. This is reminiscent of a conformational pre-equilibrium. We found only one binding pocket, namely antibody AB-C135 to remain closed along the entire minimum free energy path, suggesting an induced fit mechanism for this enzyme.

biophysics↗

bioRxiv2021

Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa to gain entry and initiate infection. Rapid detection of changes in IAV specificity towards host glycan classes can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HA) to bind sialic acids linked to larger glycan structures and a switch in HA specificity from 2,3-to 2,6-linked sialoglycans is considered a prerequisite for viral transmission from birds to humans. While the changes in HA structure associated with the evolution of binding phenotype have been mapped, the influence of glycan receptor presentation on IAV specificity remains obscured. Here, we describe a glycan array platform which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx, including glycan type and valency of the glycoconjugates and their surface density, impact IAV binding. We found that H1N1 virus produced in embryonated chicken eggs, which recognizes both receptor types, exclusively engaged mucin-mimetics carrying 2,3-linked sialic acids in their soluble form. The virus was able utilize both receptor structures when the probes were immobilized on the array; however, increasing density in the mucin-mimetic brush diminished viral adhesion. Propagation in mammalian cells produced a change in receptor pattern recognition by the virus, without altering its HA affinity, toward improved binding of 2,6-sialylated mucin mimetics and reduced sensitivity to surface crowding of the probes. Application of a support vector machine (SVM) learning algorithm as part of the glycan array binding analysis efficiently characterized this shift in binding preference and may prove useful to study the evolution of viral responses to a new host.

microbiology↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2020

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the 3-dimensional electron density of a protein, as it would be determined by cryo-EM or X-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work uses the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along reaction coordinates derived from electron densities can serve to induce conformational transitions between known intermediates. The simulated pathways appear reversible, with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein DataBank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution (~5 [A]) density information coupled to comparatively large structural transitions. Practical considerations for generating transitions with multiple intermediate atomic density distributions are also discussed.

biophysics↗