Geometric Restraints Research Articles

An Innovative Technique for the Strengthening of RC Columns and Their Connections with Beams Using C-FRP ROPES

The application of the innovative C-FRP ropes for the strengthening of reinforced concrete columns is experimentally examined. Two real-scale specimens with the same geometrical characteristics and the same steel reinforcements were constructed for the needs of this investigation. The primary objective of the study is to investigate the efficacy of the use of C-FRP ropes as externally mounted reinforcement for the strengthening of deficient external columns. In this direction, (a) C-FRP ropes are applied as longitudinal reinforcement of the column for the increase in the flexural strength, (b) C-FRP ropes are applied as external confining stirrups in the critical end parts of the column for the improvement of the concrete strength and the development of local element ductility, and finally (c) C-FRP ropes are applied as external stirrups in the form of diagonal X-shaped reinforcement for the increase in the capacity of the part of the column connected with the beam (joint panel). Both specimens are tested under the same cyclic loading procedure that comprises seven steps and each step includes three full loading cycles. The maximum loads of the strengthened specimen at the three loading cycles of the seventh step were 40%, 72% and 87% higher than the corresponding ones of the unstrengthened specimen. On the other hand, the measured shear deformations of the joint panel of the pilot (unstrengthened) specimen at the sixth and the seventh steps were 43% and 44% higher than the corresponding ones of the strengthened specimen. In general, it is concluded that the strengthened column exhibited improved hysteretic response and the whole behavior was apparently improved compared to the pilot specimen without strengthening in terms of maximum loads per loading step, dissipated energy, and shear deformations of the joint panel. In particular, it is stressed that the measured shear deformations of the joint panel and strain gauge measurements have substantiated that the column and the connection panel of the strengthened specimen remain almost intact, whereas damage and eventually failure have been located in the column and the joint panel of the pilot specimen. Additionally, it is emphasized that the C-FRP ropes can easily be applied in structures with complex configuration without any geometrical restraints.

Electrofluids with Tailored Rheoelectrical Properties: Liquid Composites with Tunable Network Structures as Stretchable Conductors.

Flexible and stretchable electronics require both sensing elements and stretching-insensitive electrical connections. Conductive polymer composites and liquid metals are highly deformable but change their conductivity upon elongation and/or contain rare metals. Solid conductive composites are limited in mechanoelectrical properties and are often combined with macroscopic Kirigami structures, but their use is limited by geometrical restraints. Here, we introduce "Electrofluids", concentrated conductive particle suspensions with transient particle contacts that flow under shear that bridge the gap between classic solid composites and liquid metals. We show how Carbon Black (CB) forms large agglomerates when using incompatible solvents that reduce the electrical percolation threshold by 1 order of magnitude compared to more compatible solvents, where CB is well-dispersed. We analyze the correlation between stiffness and electrical conductivity to create a figure of merit of first electrofluids. Sealed elastomeric tubes containing different types of electrofluids were characterized under uniaxial tensile strain, and their electrical resistance was monitored. We found a dependency of the piezoresistivity with the solvent compatibility. Electrofluids enable the rational design of sustainable soft electronics components by simple solvent choice and can be used both as sensor and electrode materials, as we demonstrate.

Insertion and Anchoring of HIV-1 Fusion Peptide into Complex Membrane Mimicking Human T-cell.

A fundamental understanding of how HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a 9-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow timescale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input. Using a novel geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single enables the prediction of interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single convincingly outperforms current state-of-the-art methods including AlphaFold 3, particularly when evolutionary information is limited; and exhibits remarkable robustness and performance resilience by attaining better accuracy with only single-sequence input than what most methods can achieve even with explicit evolutionary information. Freely available at https://github.com/Bhattacharya-Lab/ProRNA3D-single, ProRNA3D-single should be broadly useful for modeling 3D structures of protein-RNA complexes at scale, regardless of the availability of evolutionary information.

Blind Attention Geometric Restraint Neural Network for Single Image Dynamic/Defocus Deblurring.

Based on the information loss analysis of the blur accumulation model, a novel single-image deblurring method is proposed. We apply the recurrent neural network architecture to capture the attention perception map and the generative adversarial network (GAN) architecture to yield the deblurring image. Considering that the attention mechanism has to make hard decisions about specific parts of the input image to be focused on since blurry regions are not given, we propose a new adaptive attention disentanglement model based on the variation blind source separation, which provides the global geometric restraint to reduce the large solution space, so that the generator can realistically restore details on blurry regions, and the discriminator can accurately assess the content consistency of the restored regions. Since we combine blind source separation, attention geometric restraint with GANs, we name the proposed method BAGdeblur. Extensive evaluations on quantitative and qualitative experiments show that the proposed method achieves the state-of-the-art performance on both synthetic datasets and real-world blurry images.

Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction

RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.

GEMMI and Servalcat restrain REFMAC5.

Macromolecular refinement uses experimental data together with prior chemical knowledge (usually digested into geometrical restraints) to optimally fit an atomic structural model into experimental data, while ensuring that the model is chemically plausible. In the CCP4 suite this chemical knowledge is stored in a Monomer Library, which comprises a set of restraint dictionaries. To use restraints in refinement, the model is analysed and template restraints from the dictionary are used to infer (i) restraints between concrete atoms and (ii) the positions of riding hydrogen atoms. Recently, this mundane process has been overhauled. This was also an opportunity to enhance the Monomer Library with new features, resulting in a small improvement in REFMAC5 refinement. Importantly, the overhaul of this part of CCP4 has increased flexibility and eased experimentation, opening up new possibilities.

Achieving Accurate Standard Protein-Protein Binding Free Energy Calculations through the Geometrical Route and Ergodic Sampling.

A new strategy for the prediction of binding free energies of protein-protein complexes is reported in the present article. By combining an ergodic-sampling algorithm with the so-called "geometrical route", which introduces a series of geometrical restraints as a preamble to the physical separation of the two partners, we achieve accurate binding free energy calculations for medium-sized protein-protein complexes within the microsecond timescale. The ergodic-sampling algorithm, namely, Gaussian-accelerated molecular dynamics (GaMD), implicitly helps explore the conformational change of the two binding partners as they associate reversibly by raising the energy wells. Therefore, independent simulations capturing the isomerization of proteins are no longer needed, reducing both the computational cost and human effort. Numerical applications indicate errors on the order of 0.1 kcal/mol for the Abl-SH3 domain binding a decapeptide, of 2.6 kcal/mol for the barnase-barstar complex, and of 0.2 kcal/mol for human leukocyte elastase binding the third domain of the turkey ovomucoid inhibitor. Compared with the classical geometrical route, which resorts to collective variables to describe the isomerization of proteins, our new strategy possesses remarkable convergence properties and robustness for protein-protein complexes owing to improved ergodic sampling. We are confident that the strategy presented in this study will have a broad range of applications, helping us understand recognition-association phenomena in the areas of physical, biological, and medicinal chemistry.

Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states.

In the efforts to identify structure-function relationships of protein machinery, computational methods have developed rapidly as tools to assist the scientific community in both structural prediction as well as functional relationships in context of biological pathways. Although solving high resolution structural information has been clear for stable folded macromolecules, similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating and analyzing large diverse ensembles of disordered protein states. IDPConformerGenerator builds conformers that obey geometric, steric, and other physical restraints on the input sequence by sampling backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank. All-atom sidechains can be added using robust Monte Carlo algorithms with expanded rotamer libraries. IDPConformerGenerator has many user-configurable options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to provide structural insights into these states that have key biological functions. Ultimately, a better structural understanding of disordered protein ensembles can open up avenues for future research on pharmaceuticals targeting diseases mediated by IDPs.

Trimming the disordered C-terminal extension of the protease inhibitor Hg1 produces a potent Kv1.3 blocker.

Animal venom contains a mixture of toxic polypeptides that act in synergy to enable the fast immobilization of prey or deter predators and competitors. This polypeptide arsenal is constantly replenished by recruiting protein scaffolds from various organs into the venom gland. The recruited proteins often retain their ancestral functions but acquire specific modifications associated with novel toxic functions. Here we report an in-depth analysis of Hg1, a Kunitz-fold protease inhibitor with moderate blocking activity on Kv1.3, isolated from the venom gland of the Hadrurus gertschi scorpion. Electrophysiological analyses, supported by MD simulations, suggested that in contrast to canonical K+ blocking peptides, Hg1 does not insert a lysine residue into the channel pore. The crystal structure of the toxin revealed an unusually long, disordered extension at the C-terminal region of the molecule. Deletion of the C-terminal extension increased the potency of the toxin by order of magnitude and restored the voltage sensitivity of the block, a hallmark of the canonical block mechanism. In silico analysis supported the notion that removal of the disordered C-terminal extension has lifted a geometric restraint that prevents the wild-type toxin from effectively blocking Kv1.3. We propose that Hg1 is an evolutionary intermediate between a potent protease inhibitor and a potent K+ channel blocker.

In situ ligand restraints from quantum-mechanical methods.

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities.

Description of peptide bond planarity from high-resolution neutron crystallography.

Neutron crystallography is a highly effective method for visualizing hydrogen atoms in proteins. In our recent study, we successfully determined the high-resolution (1.2 Å) neutron structure of high-potential iron-sulfur protein, refining the coordinates of some amide protons without any geometric restraints. Interestingly, we observed that amide protons are deviated from the peptide plane due to electrostatic interactions. Moreover, the difference in the position of the amide proton of Cys75 between reduced and oxidized states is possibly attributed to the electron storage capacity of the iron-sulfur cluster. Additionally, we have discussed about the rigidity of the iron-sulfur cluster based on the results of the hydrogen-deuterium exchange. Our research underscores the significance of neutron crystallography in protein structure elucidation, enriching our understanding of protein functions at an atomic resolution.

Free vibration of in-plane bi-directional functionally graded materials rectangular plates with geometric imperfections and general elastic restraints

This research addresses the free vibration performance of bi-directional functionally graded materials (BDFGMs) rectangular plate with general boundary restraints and geometrical imperfections. The symmetric/asymmetric material distributions subject to power-law along in-plane two directions are considered. Two types of geometric imperfections are formulated employing transcendental functions, including the sine-type imperfection which is global eccentric/symmetrical and depicted by sine and exponential functions, and the cosine-type global/localized imperfections which is simulated by hyperbolic and cosine functions. The general boundary restraints are imitated by artificial virtual springs. The Lagrangian energy function is formulated, and then vibration characteristics of plates are acquired with the Ritz method and improved Fourier series. Verifications are carried out for accuracy of theoretical model and reliability of solution method. A comprehensive numerical investigation are exhibited to explore the influencing mechanism of material distribution patterns, geometrical imperfection modal and elastic boundary restraints on vibration performance of rectangular plates. The results show that the in-plane material gradient can function as regulatory measures for dynamic characteristic of plates. In addition, geometric imperfections may cause singularity in vibration performance of plate. These innovative results may serve as a benchmark for future studies on design or analysis of plates.

Effect of temperature and porosity on free vibration characteristics of a doubly-curved skew laminated sandwich composite structures with 3D printed PLA core

This paper deals with the investigation of the influence of temperature and porosity on the vibration response of the doubly-curved skew laminated sandwich composite (DSLSC) shells. The temperature-reliant properties of the graphite–epoxy face sheet and the 3D printed polylactic acid (PLA) core are considered for the numerical analysis. The coupled thermo-elastic finite element (FE) model is developed using the first-order shear deformation theory (FSDT) to study the influence of uniform temperature rise on the modal behavior of the DSLSC shells. The stiffness generated due to thermal exposure is accounted to introduce the initial stress stiffness matrix. Further, the initial stress stiffness matrix is developed using nonlinear strain–displacement relations, while the mechanical stiffness matrices are characterized using linear strain–displacement relations. A comprehensive parametric study has been performed to appreciate the effect of temperature, geometric restraints, and material constraints of a laminated composite sandwich spherical, hyperbolic, ellipsoidal, cylindrical shells, and flat plates. Series of experiments are performed to understand the influence of temperature on the natural frequency of glass–epoxy laminated composite sandwich plates with 3D printed PLA core. Further exploration is dedicated to comprehending the experimental challenges involved in studying the vibration performance of sandwich structures with 3D printed PLA core in the thermal environment.

DR-SLAM: drift rejection SLAM with Manhattan regularity for indoor environments

In this paper, a drift rejection SLAM (Simultaneous Localization and Mapping) method is proposed targeting indoor scenarios, where SLAM generates large drifts due to the lack of reliable features. To provide sufficient features, we leverage multiple feature primitives and geometric restraints (parallel or perpendicular) restraint in man-made environments. Under some satisfy Manhattan World (MW) assumption scene, such as corridors, we can get absolute and drift-free rotation estimation using a Gaussian sphere. By fully utilizing drift-free rotation estimation under MW assumption and the local stability of purely track restricted by point, line, and plane features, our drift rejection SLAM method becomes more accurate and robust. Additionally, by exploiting the constraint of planar motion on ground robot, we propose an ingenious strategy to reduce translation drift by eliminating vertical movement in the Manhattan world. Advantages of our method over other state-of-the-art algorithms are validated on public datasets and real-world experiments. The code is released at https://github.com/WangWen-Believer/DR-SLAM.

BRL/Pisa/IIT SoftHand: A Low-Cost, 3D-Printed, Underactuated, Tendon-Driven Hand With Soft and Adaptive Synergies

This letter introduces the BRL/Pisa/IIT (BPI) SoftHand: a single actuator-driven, low-cost, 3D-printed, tendon-driven, underactuated robot hand that can be used to perform a range of grasping tasks. Based on the adaptive synergies of the Pisa/IIT SoftHand, we design a new joint system and tendon routing to facilitate the inclusion of both soft and adaptive synergies, which helps us balance durability, affordability and grasping performance of the hand. The focus of this work is on the design, simulation, synergies and grasping tests of this SoftHand. The novel phalanges are designed and printed based on linkages, gear pairs and geometric restraint mechanisms, and can be applied to most tendon-driven robotic hands. We show that the robot hand can successfully grasp and lift various target objects and adapt to hold complex geometric shapes, reflecting the successful adoption of the soft and adaptive synergies. We intend to open-source the design of the hand so that it can be built cheaply on a home 3D-printer.

Conformational ensemble of amyloid-forming semenogelin 1 peptide SEM1(68–107) by NMR spectroscopy and MD simulations

SEM1(68–107) is a peptide corresponding to the region of semenogelin 1 protein from 68 to 107 amino acid position. SEM1(68–107) is an abundant component of semen, which participates in HIV infection enhanced by amyloid fibrils forming. To understand the causes influencing amyloid fibril formation, it is necessary to determine the spatial structure of SEM1(68–107). It was shown that the determination of SEM1(68–107) structure is complicated by the non-informative NMR spectra due to the high intramolecular mobility of peptides. The complementary approach based on the geometric restrictions of individual peptide fragments and molecular modeling was used for the determination of the spatial structure of SEM1(68–107). The N- (SEM1(68–85)) and C-terminuses (SEM1(86–107)) of SEM1(68–107) were chosen as two individual peptide fragments. SEM1(68–85) and SEM1(86–107) structures were established with NMR and circular dichroism CD spectroscopies. These regions were used as geometric restraints for the SEM1(68–107) structure modeling. Even though most of the SEM1(68–107) peptide is unstructured, our detailed analysis revealed the following structured elements: N-terminus (70His-84Gln) forms an α-helix, (86Asp-94Thr) and (101Gly-103Ser) regions fold into 310-helixes. The absence of a SEM1(68–107) rigid conformation leads to instability of these secondary structure regions. The calculated SEM1(68–107) structure is in good agreement with experimental values of hydrodynamic radius and dihedral angles obtained by NMR spectroscopy. This testifies the adequacy of a combined approach based on the use of peptide fragment structures for the molecular modeling formation of full-size peptide spatial structure.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States.

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Stress-intensity factors solutions for straight through cracks in C-sections

This work details a new stress-intensity factor (SIF) solution for a through-thickness edge crack in a structural member with a cross-section idealized as a generic C-section. This solution employs the methodology by Herrmann and co-workers (Herrmann and Sosa, 1986; Kienzler and Herrmann, 1986) that relates notch widening in structural sections to crack advance in the cross-section. Here, we calibrate an additional correction factor using a response surface methodology informed by results from finite element analyses. This approach results in an accurate SIF solution over the design space that is suitable for fatigue crack growth (FCG) analyses. Additional analyses performed in this work show the impact of changing the geometric ratios, loading, and restraint conditions on the SIF solutions.