Refinement Protocol Research Articles

Waterless structures in the Protein Data Bank.

The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 inatomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors.

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks.

Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols.

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

The Development of Computer Assisted Vocabulary Learning (CAVL) to Improve English Lexical Retention of Nursing Students

Vocabulary mastery is crucial for nursing students to effectively communicate in English. This study aimed to develop and validate a tailored computer-assisted vocabulary learning (CAVL) intervention to improve nursing students' retention of English lexicon, which is essential for healthcare communications. The CAVL program was designed using the Moodle platform and focused on four thematic units that targeted essential nursing vocabulary. The learning process followed research-based principles of vocabulary instruction, including multimodal introduction, reinforced retrieval, and contextual repetition. This study utilized a research and development methodology to conduct iterative needs analyses, design refinement, and rigorous evaluation protocols. Expert reviews, prototype testing, post-intervention vocabulary tests, and delayed assessments were used to gather data and make data-driven improvements. Quantitative analysis evaluated the effectiveness of this approach for 100 Indonesian nursing students. Vocabulary assessments were administered before, immediately after, and two weeks after the intervention, revealing significant improvements in terminology knowledge following the implementation of CAVL. Importantly, scores remained stable during the delayed assessment, demonstrating durable retention. These results are consistent with previous literature on the benefits of contextual and multimodal vocabulary learning. The CAVL prototype facilitated learning and effectively improved vocabulary retention outside of the classroom. This research provides an adaptable framework for technologically-assisted language mastery, which is essential for the next generation of nursing education. Further studies can explore the application of this framework in allied healthcare fields and the transition to practice in nursing education.

OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking.

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.

Improving resolution and resolvability of single-particle cryoEM structures using Gaussian mixture models.

Cryogenic electron microscopy is widely used in structural biology, but its resolution is often limited by the dynamics of the macromolecule. Here we developed a refinement protocol based on Gaussian mixture models that integrates particle orientation and conformation estimation and improves the alignment for flexible domains of protein structures. We demonstrated this protocol on multiple datasets, resulting in improved resolution and resolvability, locally and globally, by visual and quantitative measures.

Elsa: enhanced latent spaces for improved collider simulations

Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W + jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement (Laser) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RamboOnDiet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

Structural quantification of polar/nonpolar phases of relaxor clusters in transparent ferroelectric ceramics

A dual-phase refinement protocol considering distinct amounts of polar/nonpolar phases reflecting the polar nano phases formation in two relaxor ferroelectric systems was performed. The samples of PLZT and PLMN-PT have shown a typical relaxor behavior with different freezing temperatures, and the amounts of the polar phase of the lack of ergodicity were obtained using structure parameters.

Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.

Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein-ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination.

Efficient and Privacy-Preserving Spatial-Feature-Based Reverse kNN Query

Reverse k nearest neighbor (RkNN) query has been widely applied in the targeted push of information. Many schemes for the RkNN query on encrypted data have been proposed for coordinating the emerging trend of outsourcing data to the cloud. However, none of them supports the spatial data with many features, a prevalent data type in location-based services, e.g., each user in online dating apps usually has a spatial location and many personality trait features. Meanwhile, incorporating features with the spatial data endows the spatial-feature-based RkNN query to provide more precise services than the spatial-based RkNN query. Therefore, as a steppingstone, we propose an efficient and privacy-preserving spatial-feature-based RkNN scheme in this work for the first time. Specifically, we first design a modified intersection and union R tree (MIUR-tree) to index the spatial and feature data. Then, we introduce an MIUR-tree based RkNN query algorithm in the filter and refinement framework to efficiently process RkNN queries. After that, based on a symmetric homomorphic encryption (SHE) scheme, we design a private filter protocol and a private refinement protocol, and leverage them to propose our RkNN query scheme. Rigorous security analysis demonstrates that our scheme is privacy-preserving, and extensive experiments indicate that our scheme is computationally efficient.

Conformation-based refinement of 18-mer DNA structures.

Nine new crystal structures of CG-rich DNA 18-mers with the sequence 5'-GGTGGGGGC-XZ-GCCCCACC-3', which are related to the bacterial repetitive extragenic palindromes, are reported. 18-mer oligonucleotides with the central XZ dinucleotide systematically mutated to all 16 sequences show complex behavior in solution, but all ten so far successfully crystallized 18-mers crystallized as A-form duplexes. The refinement protocol benefited from the recurrent use of geometries of the dinucleotide conformer (NtC) classes as refinement restraints in regions of poor electron density. The restraints are automatically generated at the dnatco.datmos.org web service and are available for download. This NtC-driven protocol significantly helped to stabilize the structure refinement. The NtC-driven refinement protocol can be adapted to other low-resolution data such as cryo-EM maps. To test the quality of the final structural models, a novel validation method based on comparison of the electron density and conformational similarity to the NtC classes was employed.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR.

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

A microcomputed tomographic analysis of the morphological variabilities and incidence of extra canals in mandibular first molar teeth in an Egyptian subpopulation

A well-protected microbial habitat may be present in the root and canal morphology, which is varied and complicated. Before initiating effective root canal treatment, a detailed knowledge of the root and canal anatomical variances in each tooth is a must. This study aimed to investigate the root canal configuration, apical constriction anatomy, location of the apical foramen, dentine thickness, and prevalence of accessory canals in mandibular molar teeth in an Egyptian subpopulation using micro-computed tomography (microCT). A total of 96 mandibular first molars were scanned using microCT, and 3D reconstruction was performed using Mimics software. The root canal configurations of each of the mesial and distal root were classified with two different classification systems. The prevalence and dentin thickness around middle mesial and middle distal canals were investigated. The number, location and anatomy of major apical foramina and the apical constriction anatomy analysed. The number and location of accessory canals were identified. Our findings showed that two separate canals (15%) and one single canal (65%) were the most common configuration in the mesial and distal roots, respectively. More than half of the mesial roots had complex canal configurations and 51% had middle mesial canals. The single apical constriction anatomy was the most common for both canals followed by the parallel anatomy. Disto-lingual and distal locations of the apical foramen are the most common location for both roots. Mandibular molars in Egyptians show a wide range of variations in root canal anatomy with high prevalence of middle mesial canals. Clinicians should be aware of such anatomical variations for successful root canal treatment procedures. A specific access refinement protocol and appropriate shaping parameters should be designated for each case to fulfil the mechanical and biological objectives of root canal treatment without compromising the longevity of treated teeth.

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement.

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.

Using macromolecular phasing and refinement techniques for a small molecule gyrase inhibitor

Topoisomerases resolve DNA topoisomers, which is essential for faithful replication and transcription. Gyrases are type II topoisomerases unique to bacteria and well-known targets for antibiotics. Unfortunately, currently used gyrase inhibitors suffer from increased resistance, necessitating structure-based drug design on novel chemical classes. During such efforts, a crystal of a small molecule gyrase inhibitor grew fortuitously from an experiment aimed at a protein/ligand complex. The crystal diffracted insufficiently for standard direct phasing methods to work, and the diffraction data to 1.3 Å resolution did not contain anomalous signal for experimental phasing. Using macromolecular replacement techniques with fragments of the ligand as search models that were predicted to be planar, an initial electron density map was obtained that allowed building of a complete structure. While standard small molecule refinement was unstable, macromolecular refinement protocols were applied successfully to an Rfree of ∼20%, likely aided by the appreciably large solvent content of the small molecule crystal (34-51%, depending on how it is calculated). Comparison of the small molecule structure to a chemically related ligand in complex with the ATPase domain of P. aeruginosa gyrase reveals quite different conformations due to variations in torsion angles connecting aromatic and aliphatic moieties. Such information may prove useful in drug design. While the present case may be among the first in using “macromolecular” methods for phasing and refinement of “small-molecule data,” we hope that in the future this approach proves useful in extracting information from sub-standard small-molecule diffraction data.

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1.

With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L1043.32 and L2556.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.

Simulating diffraction photographs based on molecular dynamics trajectories of a protein crystal: a new option to examine structure-solving strategies in protein crystallography.

A molecular dynamics (MD)-based pipeline has been designed and implemented to emulate the entire process of collecting diffraction photographs and calculating crystallographic structures of proteins from them. Using a structure of lysozyme solved in-house, a supercell comprising 125 (5 × 5 × 5) crystal unit cells containing a total of 1000 protein molecules and explicit interstitial solvent was constructed. For this system, two 300 ns MD trajectories at 298 and 250 K were recorded. A series of snapshots from these trajectories were then used to simulate a fully realistic set of diffraction photographs, which were further fed into the standard pipeline for structure determination. The resulting structures show very good agreement with the underlying MD model not only in terms of coordinates but also in terms of B factors; they are also consistent with the original experimental structure. The developed methodology should find a range of applications, such as optimizing refinement protocols to solve crystal structures and extracting dynamics information from diffraction data or diffuse scattering.

Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy.

The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.

Design of electrical impedance spectroscopy sensing surgical drill using computational modelling and experimental validation

Electrical Impedance Spectroscopy (EIS) sensing surgical instruments could provide valuable and real-time feedback to surgeons about hidden tissue boundaries, therefore reducing the risk of iatrogenic injuries. In this paper, we present an EIS sensing surgical drill as an example instrument and introduce a strategy to optimize the mono-polar electrode geometry using a finite element method (FEM)-based computational model and experimental validation. An empirical contact impedance model and an adaptive mesh refinement protocol were developed to accurately preserve the behaviour of sensing electrodes as they approach high impedance boundaries. Specifically, experiments with drill-bit, cylinder, and conical geometries suggested a 15%–35% increase in resistance as the sensing electrode approached a high impedance boundary. Simulations achieved a maximum mean experiment-to-simulation mismatch of +1.7% for the drill-bit and +/−11% range for other electrode geometries. The simulations preserved the increase in resistance behaviour near the high impedance boundary. This highly accurate simulation framework allows us a mechanism for optimizing sensor geometry without costly experimental evaluation.

Refinement in the European Union: A Systematic Review.

Refining experiments and housing conditions so as to cause the minimum possible pain and distress is one of the three principles (3Rs) on which Directive 2010/63/EU is based. In this systematic review, we aimed to identify and summarize published advances in the refinement protocols made by European Union-based research groups from 2011 to 2021, and to determine whether or not said research was supported by European or national grants. We included 48 articles, the majority of which were related to improvements in experimental procedures (37/77.1%) for mice (26/54.2%) and were written by research groups belonging to universities (36/57.1%) and from the United Kingdom (21/33.9%). More than two thirds (35/72.9%) of the studies received financial support, 26 (mostly British) at a national level and 8 at a European level. Our results indicated a clear willingness among the scientific community to improve the welfare of laboratory animals, as although funding was not always available or was not specifically granted for this purpose, studies were published nonetheless. However, in addition to institutional support based on legislation, more financial support is needed. We believe that more progress would have been made in refinement during these years if there had been more specific financial support available at both the national and European Union levels since our data suggest that countries investing in refinement have the greatest productivity in successfully publishing refinements.