Journal of Chemical Theory and Computation

The flexibility of DNA and RNA is known to play a central role in numerous biological processes, including chromatin organization and gene regulation. While a wide range of computational approaches have been developed to investigate the conformational dynamics and flexibility of proteins, analogous methods for nucleic acids remain comparatively underexplored. Elastic Network Models (ENMs) - coarse-grained mechanical representations in which macromolecules are modeled as networks of nodes connected by elastic springs - have been successfully applied to proteins, often allowing to capture experimentally observed conformational changes through a small number of harmonic normal modes. Building on a previously validated three-bead ENM for RNA, here we introduce edENM, an essential dynamics-refined ENM for DNA, RNA, and protein-nucleic acid complexes, parametrized using a diverse set of Molecular Dynamics simulations. The vibrational modes of the new edENM show good agreement with NMR data and experimental ensembles, while avoiding the unrealistic and localized deformability of previous ENM parametrizations. Additionally, we integrated this new edENM into eBDIMS, a Brownian Dynamics-based framework that enables the simulation of large-scale and anharmonic conformational transitions in protein assemblies. In this way, we are now able to explore functional motions in large protein-nucleic acid complexes such as chromatin subunits and ribosomes.

Accurate ion force fields are essential for molecular dynamics simulations of biomolecular systems, particularly in combination with modern water models such as OPC. While OPC water improves the description of bulk water and biomolecules, the transferability of existing ion force fields to this model remains an open question. Here, we systematically assess the transferability of monovalent and divalent ion force field parameters (Li+, Na+, K+, Cs+, Mg2+,Ca2+, Sr2+, Ba2+, Cl- and Br-) to OPC water by comparing single-ion and ion-pairing properties with experimental data. Our analysis reveals that no single literature parameter set provides accurate results for all ions when directly transferred to OPC water. We hence introduce the MS/G-LB(OPC) force field, which combines Mamatkulov-Schwierz-Grotz cation parameters with Loche-Bonthuis anion parameters. MS/G-LB(OPC) reproduces hydration free energies, first-shell structural properties and activity derivatives at low salt concentrations. Our results demonstrate that transferring ion parameters to OPC can lead to significant and ion-specific deviations from experimental data, making careful validation essential. At the same time, the systematic transfer and combination of ion parameters from existing force fields can provide a practical and computationally efficient alternative to full reparameterization. MS/G-LB(OPC) is available at https://git.rz.uni-augsburg.de/cbio-gitpub/opc-ion-force-fields.

Understanding RNA structural dynamics is essential for elucidating its biological functions, and molecular dynamics (MD) simulations provide an important atomistic complement to experimental approaches. However, the predictive power of MD is fundamentally limited by the accuracy of the underlying empirical Force Fields (FFs), particularly in capturing the delicate balance of non-bonded interactions. Here, we present a systematic reparameterization strategy that replaces the external gHBfix19 hydrogen-bond (H-bond) correction potential with an equivalent set of NBfix Lennard-Jones modifications within a state-of-the-art RNA FF. Using a quantitatively converged temperature replica-exchange MD ensemble of the GAGA tetraloop, we employed a reweighting-based optimization protocol to derive NBfix parameters that reproduce the thermodynamic effects of the original gHBfix19 terms. Sequential optimization of individual gHBfix19 components proved essential to ensure stable and transferable parameter refinement. The resulting fully reformulated NBfix-based variant, termed OL3CP-NBfix19, was validated on a representative set of RNA motifs, including tetranucleotides, A-form duplexes, and tetraloops. Across all tested systems, its performance is comparable to that of the reference gHBfix19 FF. By embedding the H-bond corrections directly into the standard non-bonded framework, the NBfix formulation eliminates external biasing potentials, simplifies practical deployment, and reduces computational overhead. Beyond this specific reparameterization, our results demonstrate a practical workflow for translating targeted H-bond corrections into native FF terms for efficient biomolecular simulations.

Molecular dynamics simulations provide a "computational microscope" by which molecular phenomena can be studied at atomic resolution. However, such simulations are often expensive, usually due to a combination of system size and timescale. Various enhanced sampling methods have been proposed to overcome these challenges. Despite their effectiveness, many suffer from artifacts from energetic biases guiding the simulations, or lack of effective progress coordinates. Proteins normal modes uniquely defined by their 3D fold capture their intrinsic dynamics and could provide unbiased guidance, but how to combine these modes with molecular dynamics to generate continuous, energetically unbiased pathways has been challenging. In this study, we demonstrate that conformations generated along from normal modes using adaptive anisotropic network model provide a physical, intuitive, and generalizable progress coordinate for weighted ensemble simulations, providing a boost in efficiency and a means to generate pathways for any protein system without prior knowledge.

Molecular dynamics (MD) simulations are a powerful tool for investigating biomolecular dynamics underlying biological functions. However, the accessible spatiotemporal scales of conventional all-atom simulations remain limited by high computational costs. Coarse-graining reduces these costs by decreasing the number of interaction sites and enabling longer timesteps. In extreme cases, proteins are represented as single spherical particles; while such approximations facilitate cellular-scale simulations, they often sacrifice essential structural information, such as molecular shape and interaction anisotropy. Here, we present CGRig, a rigid-body protein model with residue-level interaction sites designed for long-time, large-scale simulations. In CGRig, each protein is treated as a single rigid-body embedding residue-level interaction sites. Its translational and rotational motions are described by the overdamped Langevin equation incorporating a shape-dependent friction matrix. Intermolecular interactions are calculated using G[o]-like native contact potentials, Debye-Huckel electrostatics, and volume exclusion. We validated that CGRig accurately reproduces the translational and rotational diffusion coefficients expected from the friction matrix for an isolated protein. For dimeric systems, the model successfully maintained native complex structures. Furthermore, two initially separated proteins converged into the correct complex with an association rate consistent with all-atom simulations. Notably, CGRig achieved a simulation performance exceeding 17 s/day for a 1,024-molecule system. These results demonstrate that CGRig provides an efficient framework for simulating protein assembly while retaining residue-level interaction specificity, making it a valuable tool for investigating large-scale biomolecular self-assembly.

Molecular conformations play a critical role in determining molecular properties such as membrane permeability, binding affinity, and ultimately therapeutic efficacy. Both experimental and computational approaches can characterize conformations within local energy minimum, and calculations of conformational free energy provide insight into why certain conformations are thermodynamically preferred over others. However, focusing solely on sampled conformations provides only a static view of the conformational landscape which may not fully illustrate why molecules result in such a conformational ensemble. Conformational transition between different conformations further explains how and why of the conformations which further inform molecule design. Here, we introduce Internal Coordinate Net (ICoN) version 1 (v1), a deep learning model trained in MD simulation data to learn the underlying physics that governed cyclic peptide conformational dynamics. ICoN-v1 enables the identification of transient conformations and all torsion rotations between local energy minima. By following the minimum-energy pathway (MEP) in the models latent space, ICoN-v1 efficiently generates fully atomistic transition pathways that capture detailed backbone and side-chain interactions governed by concerted torsional rotations. Notably, ICoN-v1 produces smooth transition pathways that are absent from the training data, demonstrating strong generalization beyond the sampled MD conformations. Analysis of the resulting concerted torsional motions and transient states highlights key residues involved at different stages of the transition, providing mechanistic insight that can inform cyclic peptide design and drug discovery.

Coarse-grained structure-based models (CG-SBMs; or G[o] models) are simplified potential energy functions of biomolecules or biomolecular complexes that encode their structure. Molecular dynamics simulations of such SBMs have been successfully used to study long time-scale dynamics such as protein and RNA folding, and large conformational transitions of biomolecular complexes. SBMs have several advantages: (1) Their MD simulations are computationally inexpensive, making extensive sampling easily accessible to many researchers. (2) They are easy to modify and can be adapted for the specific biomolecular problem that needs to be investigated. However, the force-fields of SBMs are not usually included in commonly used biomolecular simulation packages resulting in a barrier to their use. Here, we present SuBMIT (Structure Based Models Input Toolkit; https://github.com/sglabncbs/submit), a toolkit for generating coarse-grained SBM input files for performing MD simulations with GROMACS and OpenMM/OpenSMOG. Simulations whose input files can be generated using the different flavors of CG-SBMs present in SuBMIT include the folding and conformational ensembles of proteins with intrinsically disordered regions, 3D-domain-swapping in proteins and the dynamics of RNA-protein assemblies (e.g., simple RNA viruses).

All-atom simulations of RNA using molecular dynamics have the promise of modeling conformational preferences, folding thermodynamics, conformational change kinetics, and binding affinities of small molecule therapeutics. These simulations rely on a force field, a set of equations and parameters that model the potential energy as a function of conformation using classical mechanics. One popular force field for RNA is Amber OL3, with the most recent iteration derived in 1999 and with subsequent updates to backbone dihedral parameters. The Amber force field, while frequently used, is known to have limitations; for example, it does not properly stabilize native structures against alternative structures. Here, we provide a new approach to fitting the non-bonded parameters for the force field, specifically atom-centered point charges for electrostatics and the Lennard-Jones parameters. The parameters are fit to quantum mechanics (QM) interaction energies calculated with symmetry-adapted perturbation theory (SAPT), including embedded point charges to represent the electrostatic field from solvent and adjacent nucleotides. In this pilot study with a limited set of fitting data, we use the Amber ff99 equations and atom types unchanged. With the revised parameters, we observe improvement in the stability of native structures relative to alternative structures. Native tetraloop conformations, which unfold with the Amber OL3 force field, are stable on the microsecond timescale with our new force field parameters. We also see improvement in the conformational preferences of tetramers. Crucially, A-form helices are still well-modeled, but we observe additional flexibility in an internal loop that is not consistent with NMR data. Overall, we provide evidence that this new approach to fitting RNA force field parameters to SAPT interaction energies with native-structure context represented as embedded point charges is promising. It offers a flexible solution for revising the equations in future work or for extension to other molecules that interact with RNA, such as proteins and small molecules. We call this new set of force field parameters Amber RNA.ROC26.

Characterizing conformational transitions between distinct structural states is essential for understanding protein function but remains challenging due to the timescale limitations of atomistic molecular dynamics. While coarse-grained models like Martini accelerate sampling, classical elastic-network or G[o]-like restraints often trap proteins in a single energy basin, precluding the study of transition pathways between distinct functional states. Here, we present CTGoMartini, a comprehensive Python package designed to simulate protein conformational transitions using G[o]-Martini models in explicit membranes. CTGoMartini addresses key methodological limitations of existing approaches by redefining native contacts as a dedicated interaction type, thereby eliminating spurious protein aggregation artifacts in multi-copy simulations. The package implements both switching and multiple-basin approaches (Exponential and Hamiltonian mixing) to sample transitions between experimentally defined states. Furthermore, it integrates Hamiltonian replica exchange molecular dynamics (HREMD) with PyMBAR analysis, enabling efficient optimization of mixing parameters that govern barrier heights and relative state stabilities. We demonstrate the power of CTGoMartini through two biologically significant membrane protein systems: (1) capturing the inward-open to outward-open transition of the lipid transporter SPNS2, revealing the molecular mechanism of S1P translocation; and (2) elucidating how membrane surface tension and anionic lipids (POPA, PIP2) modulate the conformational equilibrium of the mechanosensitive ion channel TREK1. By streamlining model construction, simulation, and analysis, CTGoMartini offers an easy-to-use platform that connects static structural snapshots with their underlying dynamic functional mechanisms. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/721921v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@75eb26org.highwire.dtl.DTLVardef@1a12accorg.highwire.dtl.DTLVardef@e927org.highwire.dtl.DTLVardef@1cb0dcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are are a family of voltage-gated, cyclic-nucleotide modulated Na+/K+ channels that regulate spontaneous rhythmic electrical activity in both the heart and the brain. Understanding differences in the responsiveness to cyclic adenosine monophosphate (cAMP) modulation between HCN isoforms would offer insight into the specific binding interactions that drive channel activation. Using all-atom molecular dynamics (MD) simulations and the free-energy perturbation (FEP) approach, we determined the absolute binding free energy of cAMP to the the cyclicnucleotide-binding domain (CNBD) of HCN isoforms 1-4. By studying the free-energy of ligand binding to the various isoforms of HCN, our study advances the understanding of HCN channel activation and modulation mechanisms. Overall, our work offers insight into explaining differences in channel sensitivity across the isoforms of HCN.

The catalytic mechanism of the hairpin ribozyme has remained controversial for more than two decades, with different experimental approaches often supporting distinct mechanistic interpretations. In this work, we investigate the conformational landscape of the active site along several proposed reaction pathways using all-atom molecular dynamics simulations in explicit solvent combined with enhanced sampling techniques. Specifically, we employ Hamiltonian replica exchange simulations to extensively explore active-site conformations without relying on predefined collective variables, enabling a broad characterization of the structural ensembles associated with multiple protonation states along three candidate reaction mechanisms. Our simulations suggest that a dianionic general acid/general base pathway involving direct participation of A38 and G8 is unlikely to proceed through well-defined intermediates with catalytically competent geometries. In particular, states associated with G8 deprotonation and subsequent O2 deprotonation exhibit strongly distorted active-site arrangements that appear poorly suited for reaction progression. Although highly synchronous protontransfer steps cannot be excluded, the required deprotonation of G8 remains difficult to reconcile with neutral pH conditions. In contrast, monoanionic pathways in which the non-bridging oxygens of the scissile phosphate act as transient proton relays produce intermediates that sample geometries favorable for the nucleophilic addition and leaving-group elimination steps of the reaction. These mechanisms do not require direct catalytic involvement of G8 while remaining compatible with potential acid catalysis by protonated A38+. Our results provide a unified conformational perspective on competing mechanistic scenarios. The ensembles generated here offer a foundation for future QM/MM and ML/MM calculations aimed at quantitatively resolving the free-energy landscapes governing hairpin ribozyme catalysis. Finally, the present strategy could easily be applied to other biomolecular systems with high conformational plasticity, including other ribozymes.

A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements show that this variant has at least twice as much helical structure as the wild-type U1A and is trimeric in solution, in contrast to the monomeric wild type. In sharp contrast, structures predicted by deep-learning AI tools (AlphaFold2 and RoseTTAFold2) and transformer-based tools (OmegaFold and ESMFold) are all highly similar to the wild-type U1A (backbone RMSD < 1 [A]). Even more surprising, two of the substituted ionizable residues are predicted to be fully buried in the non-polar core of the protein, an outcome that contradicts well-established physico-chemical principles, as ionizable residues are normally located on the protein surface. To explore this effect further, we generated sequences containing up to all twelve residues that make up the non-polar core of U1A. Across thousands of sequences, and depending on the AI model used, the majority of predicted structures contained fully buried ionizable residues while still maintaining the overall U1A fold. We then examined two additional proteins of comparable size, acylphosphatase and the de novo-designed TOP7 fold, and observed the same phenomenon: AI models frequently predicted structures with buried ionizable residues that nevertheless retained the parent fold. When these AI-predicted structures were subjected to short (50 ns) molecular dynamics simulations using physics-based force fields such as CHARMM or AMBER, the structures rapidly relaxed into ensembles that exposed ionizable residues. We conclude that while AI-based structure prediction tools perform extremely well on naturally occurring sequences, they do not reliably encode the physico-chemical principles governing the placement of ionizable residues. A straightforward remedy is to include a brief molecular dynamics simulation as a final validation step for AI-generated structures.

Wild-type T4 lysozyme (T4L) is used as a benchmark to evaluate conformational sampling across generative AI, AI-accelerated molecular simulation (AMS), and physics-based enhanced molecular dynamics (EMD). A four-state model: exposed/open, exposed/closed, buried/open, and buried/closed; is defined using physically meaningful collective variables. While generative AI methods (AF-cluster, MSA subsampling of AlphaFold2, ConforFold, AlphaFlow, ESMFlow, ConfRover, BioEmu) largely sample only the exposed/open state, AMS integrating generative ensembles with iterative molecular dynamics, recovering all states and reproducing equilibrium populations similar to EMD and experimental smFRET signatures.

Protein kinases regulate signaling by recognizing short sequence motifs, and how these motifs bind influences both specificity and therapeutic strategies that target kinase pathways. Peptide-based inhibitors that engage substrate-recognition regions are attracting interest, but designing them requires an understanding of how a flexible peptide approaches and settles into the bound pose. Traditional studies have focused on the bound pose and affinities, whereas the steps that link the initial encounter with the bound pose have been explored less thoroughly because the relevant intermediates are too short-lived to capture experimentally and evolve on timescales that standard molecular dynamics cannot readily access. Here, we focused on Abl kinase and Abltide, the experimentally identified optimal substrate peptide for Abl kinase, and examined the sequence of events linking initial encounter to the bound pose using two-dimensional replica exchange (gREST/REUS), which selectively enhances flexibility in the peptide and its binding interface while also sampling progression along a distance coordinate. The resulting simulations yielded a detailed binding landscape, revealing five distinct encounter regions outside the substrate-binding site and six intermediate states that may connect the initial approach to the bound pose. Some encounter regions and intermediate states participate in the dominant binding pathways. During this process, EF/G/{beta}11 hydrophobic patch, together with G helix negative patch, plays a central role in guiding Abltide toward the substrate-binding site. These findings provide mechanistic insight into substrate recognition by protein kinases and offer a foundation for the rational design of peptide-based inhibitors.

The incorporation of non-canonical amino acids (ncAAs) is a powerful strategy for introducing novel chemical functions into proteins. Molecular dynamics (MD) simulations are essential for understanding the structural and dynamic effects of these modifications, yet the creation of accurate force field parameters for ncAAs remains a significant bottleneck. Current parameterisation methods are often inaccurate or computationally expensive. To address this, we present drFrankenstein, an automated pipeline for generating AMBER force field parameters for ncAAs. drFrankenstein is a robust and accessible tool that streamlines the parameterisation workflow, enabling the routine use of MD simulations to study the behaviour of ncAA-containing proteins.

Protein function often involves multiple conformational states. Several multiple sequence alignment-perturbing strategies, including stochastic subsampling, clustering, and column masking, have been shown to enhance AlphaFold2 (AF2) sampling of alternative protein states. Here, we evaluate these strategies on AlphaFold3 (AF3) and compare their performance with the BioEmu Boltzmann sampling model on 107 proteins with multiple experimentally solved conformational states. We find that unperturbed AF3 samples alternative states with significantly higher TM-scores compared to AF2 and comparable to BioEmu. In particular, all MSA perturbation methods improve AF3 sampling at a statistically significant level, improving the top 1% TM-score by at least 0.05 in approximately 20% of cases each, while rarely worsening the performance. Furthermore, we find that different choices of amino acid masks can improve column-masked AF3 sampling for specific targets. Our results highlight how MSA perturbations remain relevant in AF3, providing a useful tool for understanding dynamic biological processes.

Protein dynamics are essential to biological function, yet understanding whether deep learning models contain information about these dynamics remains an open question. In this study, we quantitatively investigate the capacity of deep learning structure generation methods to predict protein flexibilities by directly comparing residue-level mean squared fluctuation (MSF) profiles derived from structural ensembles with experimental or simulation-informed flexibility profiles. We assembled four diverse benchmark datasets representing different types of structural information, including 70 NMR ensembles, 43 X-ray crystallographic protein pairs in two distinct conformational states, 82 high-resolution cryo-EM structures, and molecular dynamics simulations of 10 proteins. Utilizing AlphaFold3, AlphaFold2, and RosettaFold to generate multiple structural models, we applied ranksort normalization to place the profiles on a comparable scale and quantified similarity primarily using cosine and Pearson similarities. Our results demonstrate that the flexibility predictions from deep learning-generated models agree well with experimental data, suggesting that fluctuations in these predicted ensembles can serve as effective proxies for protein flexibility. Notably, AlphaFold3 consistently produced the best results across the datasets. We also observed that flexibility prediction accuracy generally improves as the number of models increases up to 15, and our findings remain robust even when terminal residues are excluded from the analysis. To facilitate broader application, we provide three publicly accessible Jupyter Notebooks to calculate MSF from deep learning outputs. Ultimately, this work provides evidence that deep learning structural ensembles can serve as proxies for protein flexibility.

Reconstructing all-atom (AA) structures from highly coarse-grained (HCG) models remains a significant challenge in multiscale molecular dynamics (MD) simulations, particularly for mesoscale biomolecular assemblies that are beyond the reach of conventional MD methods. Building upon ProNet Backmapping, a neural-network-based thermodynamically consistent approach, we introduce a progressive backmapping framework that reconstructs AA models in a stepwise manner across neighboring resolutions, for example, from a 3-residue-per-site HCG model to a 1-residue-per-site model, then to an AA model. This progressive backmapping method achieves high accuracy across a wide range of proteins and effectively reconstructs flexible linkers in multidomain architectures. Moreover, it supports hierarchical reconstruction of complex protein assemblies, including multiple virus-like particles spanning tens of nanometers and containing hundreds of subunits. Using this framework, we demonstrate--for the first time--the ability to hierarchically backmap entire viral assemblies from HCG to full AA resolution, covering at least three different resolutions. Overall, our method provides a scalable framework for incorporating atomistic detail into mesoscale simulations of complex systems across many applications in chemistry and biology. Table of contents figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/709104v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@4af423org.highwire.dtl.DTLVardef@e2669borg.highwire.dtl.DTLVardef@1be80eforg.highwire.dtl.DTLVardef@2e679_HPS_FORMAT_FIGEXP M_FIG C_FIG

We developed AlphaUnfold, an automated pipeline that couples AF3 predictions with short-time (5 ns) high-pressure Molecular Dynamics (MD) using NAMD3. By subjecting models to baric stress, AlphaUnfold acts as a dynamic "stress-test" to identify structural fragility and potential unfolding. Testing a diverse set of proteins revealed a significant inverse correlation between average pLDDT and Root Mean Square Deviation (RMSD) after MD, indicating that lower confidence translates to rapid structural drift. Furthermore, domains with low local pLDDT consistently exhibited high Root Mean Square Fluctuation (RMSF), a behavior also observed in 200 ns simulations under standard pressure, pinpointing specific metastable areas. AlphaUnfold thus provides a viable, computationally efficient framework for assessing the biophysical robustness of AI-generated models, offering an "experimental-like" validation that ensures more reliable downstream applications in structural biology. MotivationAlphaFold3 (AF3) provides high-accuracy protein models characterized by the Predicted Local Distance Difference Test (pLDDT). However, these static predictions may harbor "not well-forged" regions lacking thermodynamic resilience. There is a critical need for rapid computational protocols to validate structural integrity beyond static confidence scores. AvailabilityGitHub: https://github.com/pegados/pipeline_AlphaUnfold Supplementary informationSupplementary data are available at http://biodados.icb.ufmg.br/alphaunfold Contacte-mail fabio, silva-jrp.miguel@ufmg.br

The rational design of biomolecule immobilization strategies requires molecular-level understanding of how surface properties, tethering geometry, and structural dynamics jointly influence stability and function. Recently, coarse-grained molecular dynamics simulations based on the Martini force field have emerged as an efficient framework for studying enzyme-surface interactions. However, the reproducible construction of immobilized systems with controlled orientations remains technically challenging, usually involving multiple computational tools. Here we present MartiniSurf, an open-source command-line framework for the preparation of protein and DNA systems immobilized on solid supports within the Martini paradigm. MartiniSurf integrates automated structure retrieval and cleaning, coarse graining via tools from the Martini force field software ecosystem, customizable surface generation, and biomolecule orientation based on user-defined anchoring residues, producing complete GROMACS-ready simulation systems. The framework supports both implicit restraint-based anchoring and explicit linker-mediated immobilization, including surfaces functionalized with user-defined ligands or linker-like moieties, enabling representation of mono- and multivalent attachment geometries at different modeling resolutions. Structure-based G[o]Martini potentials can be incorporated for proteins, while DNA systems are modeled using Martini 2. Optional substrate insertion, pre-coarse-grained complex handling, and automated solvation and ionization further extend system flexibility. By integrating these components into a unified workflow, MartiniSurf enables systematic and high-throughput in silico exploration of surface-tethered biomolecules and provides a robust computational platform for rational immobilization studies. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/714767v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@bc1ac4org.highwire.dtl.DTLVardef@1813b43org.highwire.dtl.DTLVardef@159b19borg.highwire.dtl.DTLVardef@19b60d6_HPS_FORMAT_FIGEXP M_FIG C_FIG