Computational Protocol Research Articles

β-Diketonate Coordination: Vibrational Properties, Electronic Structure, Molecular Topology, and Intramolecular Interactions. Beryllium(II), Copper(II), and Lead(II) as Study Cases.

Nine metal complexes formed by three symmetric β-diketonates (viz., acetylacetonate (acac), 1,1,1,3,3,3-hexafluoro-acetylacetonate (hfac), and 2,2,6,6-tetramethylheptane-3,5-dionate (tmhd)) and three metal ions (with three different coordination geometries, viz., BeII - tetrahedral, CuII - square planar, and PbII - "swing" square pyramidal) were investigated. The study combines structural analyses, vibrational spectroscopic techniques, and quantum chemical calculations with the aim of bridging crystal structure, electronic structure, molecular topology, and far-infrared (FIR) spectroscopic characteristics. The effect of intramolecular interactions on the structural, electronic, and spectroscopic features is the center of this study. The crystal structure of Be(tmhd)2 is also reported and discussed for the first time. A complete review of the experimental IR spectra is offered; discrepancies in the assignments of some peaks are revealed among the published works. Anharmonic effects were considered for acac complexes; however, they were negligible for the FIR modes. A systematic comparison between computed and experimentally measured data allowed us to design an inexpensive, yet efficient computational protocol to investigate large polynuclear complexes.

Circuit Privacy for FHEW/TFHE-Style Fully Homomorphic Encryption in Practice

A fully homomorphic encryption (FHE) scheme allows a client to encrypt and delegate its data to a server that performs computation on the encrypted data that the client can then decrypt. While FHE gives confidentiality to clients' data, it does not protect the server's input and computation. Nevertheless, FHE schemes are still helpful in building delegation protocols that reduce communication complexity, as the ciphertext's size is independent of the size of the computation performed on them. We can further extend FHE by a property called circuit privacy, which guarantees that the result of computing on ciphertexts reveals no information on the computed function and the inputs of the server. Thereby, circuit private FHE gives rise to round optimal and communication efficient secure two-party computation protocols. Unfortunately, despite significant efforts and much work put into the efficiency and practical implementations of FHE schemes, very little has been done to provide useful and practical FHE supporting circuit privacy. In this work, we address this gap and design the first randomized bootstrapping algorithm whose single invocation sanitizes a ciphertext and, consequently, serves as a tool to provide circuit privacy. We give an extensive analysis, propose parameters, and provide a C++ implementation of our scheme. Our bootstrapping can sanitize a ciphertext to achieve circuit privacy at an 80-bit statistical security level in between 1.3 and 0.9 seconds, depending which Gaussian sampling algorithm is used, and whether the parameter set targets a fast Fourier or a number theoretic transform-based implementation. In addition, we can perform non-sanitized bootstrapping in around 0.27 or 0.14 seconds. Crucially, we do not need to increase the parameters to perform computation before or after sanitization takes place. For comparison's sake, we revisit the Ducas-Stehlé washing machine method. In particular, we give a tight analysis, estimate efficiency, review old, and provide new parameters.

Communication Efficient Secure and Private Multi-Party Deep Learning

Distributed training that enables multiple parties to jointly train a model on their respective datasets is a promising approach to address the challenges of large volumes of diverse data for training modern machine learning models. However, this approach immedi- ately raises security and privacy concerns; both about each party wishing to protect its data from other parties during training and preventing leakage of private information from the model after training through various inference attacks. In this paper, we ad- dress both these concerns simultaneously by designing efficient Differentially Private, secure Multiparty Computation (DP-MPC) protocols for jointly training a model on data distributed among multiple parties. Our DP-MPC protocol in the two-party setting is 56-794× more communication-efficient and 16-182× faster than previous such protocols. Conceptually, our work simplifies and improves on previous attempts to combine techniques from secure multiparty computation and differential privacy, especially in the context of ML training.

Securing Drug Traceability: Block chain-Enhanced Privacy Protection and Anti-Counterfeit Measures in Pharmaceutical Supply Chains

The pharmaceutical industry encounters numerous challenges in the management of medications and ensuring their authenticity, as well as safeguarding sensitive information within the supply chain. Maintaining the integrity of drug manufacturing processes, transaction records, and patient data from unauthorized access or tampering is crucial. Any breach in security could undermine trust throughout the entire supply chain. To mitigate these concerns, a multi-layered approach is employed. Initially, data encryption using QR codes with Attribute-Based Encryption provides a foundation for securing information. This is followed by an innovative strategy that combines Red Panda Optimization (RPO) Algorithm and Group Teaching Optimization algorithms (GTOA) to optimize encryption key selection. Finally, Multi-Party Computation (MPC) protocols along with Shamir's Secret Sharing enhances overall security measures. These procedures ensure that only authorized individuals have access to critical information essential for identifying counterfeit products and maintain confidentiality through Secure MPC verification without compromising sensitive details.

Private Computation on Common Fuzzy Records

Private computation on common records refers to analyze data from two databases containing shared records without revealing personal information. As a basic requirement for private computation, the databases involved essentially need to be aligned by a common identification system. However, it is hard to expect such common identifiers in real world scenario. For this reason, multiple quasi-identifiers can be used to identify common records. As some quasi-identifiers might be missing or have typos, it is important to support fuzzy records setting. Identifying common records using quasi-identifiers requires manipulation of highly sensitive information, which could be privacy concerns. This work studies the problem of enabling such data analysis on the fuzzy records of quasi-identifiers. To this end, we propose "ordered threshold-one (OTO)" matching which can be efficiently realized by circuit-based private set intersection~(CPSI) protocols and some multiparty computation (MPC) techniques. Furthermore, we introduce some generic encoding techniques from traditional matching rules to the OTO matching. Finally, we achieve a secure efficient private computation protocol which supports various matching rules which have already been widely used. We also demonstrate the superiority of our proposal with experimental validation. First, we empirically check that our encoding to OTO matching does not affect accuracy a lot for the benchmark datasets found in the fuzzy record matching literature. Second, we implement our protocol and achieve significantly faster performance at the cost of communication overhead compared to previous privacy-preserving record linkage (PPRL) protocols. In the case of 100K records for each dataset, our work shows 147.58MB communication cost, 10.71s setup time, and 1.97s online time, which is 7.78 times faster compared to the previous work (50.12 times faster when considering online time only).

Lightweight Two-Party Secure Sampling Protocol for Differential Privacy

Secure sampling is a secure multiparty computation protocol that allows a receiver to sample random numbers from a specified non-uniform distribution. It is a fundamental tool for privacy-preserving analysis since adding controlled noise is the most basic and frequently used method to achieve differential privacy. The well-known approaches to constructing a two-party secure sampling protocol are transforming uniform random values into non-uniform ones by computations (e.g., logarithm or binary circuits) or table-lookup. However, they require a large computational or communication cost to achieve a strong differential privacy guarantee. This work addresses this problem with our novel lightweight two-party secure sampling protocol. Our protocol consists of random table-lookup from a small table with the 1-out of-n oblivious transfer and only additions. Furthermore, we provide algorithms for making a table to achieve differential privacy. Our method can reduce the communication cost for (1.0, 2^(-40))-differential privacy from 183GB (naive construction) to 7.4MB.

Probing non-peptide agonists binding at the human nociceptin/orphanin FQ receptor: a molecular modelling study.

The N/OFQ-NOP receptor is a fascinating peptidergic system with the potential to be exploited for the development of analgesic drugs devoid of side effects associated with classical opioid signalling modulation. To date, up to four X-ray and cryo-EM structures of the NOP receptor in complex with the endogenous peptide agonist N/OFQ and three small molecule antagonists have been solved and released. Despite the available structural information, the details of selective small molecule agonist binding to the NOP receptor in the active state remain elusive. In this study, by leveraging the available structural information and using N/OFQ(1-13)-NH2 as a reference compound, we developed a computational protocol based on docking followed by short molecular dynamics (MD) simulations that can suggest small molecule agonist binding modes at the NOP receptor that are reproducible and stable over time in the solvated membrane-embedded receptor active state and in agreement with known structure-activity relationship (SAR) data.

A Computational Approach to Modeling Excitation Energy Transfer and Quenching in Light-Harvesting Complexes.

Light-harvesting complexes (LHCs) play a critical role in modulating energy flux within photosynthetic organisms in response to fluctuating light. Under high light conditions, they activate quenching mechanisms to mitigate photodamage. Despite their importance, the molecular mechanisms underlying these photoprotective processes remain incomplete. Herein, we present a computational protocol to model the energy pathways in the LHC, focusing specifically on the minor CP29 antenna complex of plants. We explore the factors that modulate the switch between the light-harvesting and quenched states. The protocol includes modeling the exciton Hamiltonian of the chlorophylls/lutein aggregate and calculating population dynamics using a kinetic model based on the Redfield-Förster approach. Our analysis reveals a highly tunable excited-state lifetime for the complex, that can switch between quenched and unquenched state depending on the excitation energy of the lutein, which acts as a final quencher, in accordance with recent experiments. Moreover, we observe that the s-trans lutein conformers are more likely to exhibit characteristics of the quencher.

Interactions Between [PdX4]2- (X=Cl, Br) Dianions in Presence of Counterions.

The interaction between two square palladium (II) dianions PdX4 2- (X=Cl, Br) is evaluated by crystal study and analyzed by quantum chemical means. The arrangement within the crystal between each pair of PdX4 2- neighbors is suggestive of a Pd⋅⋅⋅X noncovalent bond, which is verified by a battery of computational protocols. While the potential between these two bare dianions is computed to be highly repulsive, the introduction of even just two counterions makes this interaction attractive, as does the presence of a constellation of point charges. It is concluded that there is indeed a stabilizing Pd⋅⋅⋅X bond, but it is incapable of overcoming the strong coulombic repulsive force between two dianions. While the QTAIM, NBO, and NCI tools can indicate the presence of a noncovalent bond, they are unable to distinguish an attractive from a repulsive interaction.

Molecular basis of the CYFIP2 and NCKAP1 autism-linked variants in the WAVE regulatory complex.

The WAVE regulatory pentameric complex regulates actin remodeling. Two components of it (CYFIP2 and NCKAP1) are encoded by genes whose genetic mutations increase the risk for autism spectrum disorder (ASD) and related neurodevelopmental disorders. Here, we use a newly developed computational protocol and hotspot analysis to uncover the functional impact of these mutations at the interface of the correct isoforms of the two proteins into the complex. The mutations turn out to be located on the surfaces involving the largest number of hotspots of the complex. Most of them decrease the affinity of the proteins for the rest of the complex, but some have the opposite effect. The results are fully consistent with the available experimental data. The observed changes in the WAVE regulatory complex stability might impact on complex activation and ultimately play a role in the aberrant pathway of the complex, leading to the cell derangement associated with the disease.

Computer Folding of Parallel DNA G-Quadruplex: Hitchhiker's Guide to the Conformational Space.

Guanine quadruplexes (GQs) play crucial roles in various biological processes, and understanding their folding pathways provides insight into their stability, dynamics, and functions. This knowledge aids in designing therapeutic strategies, as GQs are potential targets for anticancer drugs and other therapeutics. Although experimental and theoretical techniques have provided valuable insights into different stages of the GQ folding, the structural complexity of GQs poses significant challenges, and our understanding remains incomplete. This study introduces a novel computational protocol for folding an entire GQ from single-strand conformation to its native state. By combining two complementary enhanced sampling techniques, we were able to model folding pathways, encompassing a diverse range of intermediates. Although our investigation of the GQ free energy surface (FES) is focused solely on the folding of the all-anti parallel GQ topology, this protocol has the potential to be adapted for the folding of systems with more complex folding landscapes.

Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations.

Military personnel involved in weapon training are subjected to repeated low-level blasts. The prevailing method of estimating blast loads involves wearable blast gauges. However, using wearable sensor data,blast loads to the head or other organscannot be accurately estimated without knowledge of the service member's body posture. An image/video-augmented complementary experimental-computational platform for conducting safer weapon training was developed. This study describes the protocol for the automated generation of weapon training scenes from video data for blast exposure simulations. The blast scene extracted from the video data at the instant of weapon firing involves the service member body avatars, weapons, ground, and other structures. The computational protocol is used to reconstruct service members' positions and postures using this data. Image or video data extracted from service member body silhouettes are used to generate an anatomical skeleton and the key anthropometric data. These data are used to generate the 3D body surface avatars segmented into individual body parts and geometrically transformed to match extracted service member postures. The final virtual weapon training scene is used for 3D computational simulations of weapon blast wave loading on service members. The weapon training scene generator has been used to construct 3D anatomical avatars of individual service member bodies from images or videos in various orientations and postures. Results of the generation of a training scene from shoulder-mounted assault weapon system and mortar weapon system image data are presented. The Blast Overpressure (BOP) tool uses the virtual weapon training scene for 3D simulations of blast wave loading on the service member avatar bodies. This paper presents 3D computational simulations of blast wave propagation from weapon firing and corresponding blast loads on service members in training.

Advancing 19F NMR Prediction of Metal-Fluoride Complexes in Solution: Insights from Ab Initio Molecular Dynamics.

19F NMR parameters are versatile probes for studying metal-fluoride complexes. Quantum chemical calculations of 19F NMR chemical shifts enhance the accuracy and validity of the resonance signal assignments in complex spectra. However, the treatment of solvation effects in these calculations remains challenging. In this study, we establish a successful computational protocol using ab initio molecular dynamics simulations for the accurate prediction of 19F NMR chemical shifts in square-planar nickel-fluoride complexes. In particular, we have studied in detail the trans-[NiF(2,3,4,5-C6F4I)(PEt3)2] complex in a benzene solution. Our computations revealed that accounting for the dynamic conformational flexibility of the complex, including intramolecular interactions, is crucial for obtaining reliable 19F NMR chemical shifts. Overall, this study advances the understanding of employing state-of-the-art quantum chemistry methods to accurately model 19F NMR chemical shifts of nickel-fluoride complexes, emphasizing the importance of addressing solvation effects in such calculations.

Tunable Generation of Spatial Entanglement in Nonlinear Waveguide Arrays.

Harnessing high-dimensional entangled states of light presents a frontier for advancing quantum information technologies, from fundamental tests of quantum mechanics to enhanced computation and communication protocols. In this context, the spatial degree of freedom stands out as particularly suited for on-chip integration. But while traditional demonstrations produce and manipulate path-entangled states sequentially with discrete optical elements, continuously coupled nonlinear waveguide systems offer a promising alternative where photons can be generated and interfere along the entire propagation length, unveiling novel capabilities within a reduced footprint. Here we exploit this concept to implement a compact and reconfigurable source of path-entangled photon pairs based on parametric down-conversion in semiconductor nonlinear waveguide arrays. We use a double-pump configuration to engineer the output quantum state and implement various types of spatial correlations, exploiting a quantum interference effect between the biphoton state generated in each pumped waveguide. This demonstration, at room temperature and telecom wavelength, illustrates the potential of continuously coupled systems as a promising alternative to discrete multicomponent quantum circuits for leveraging the high-dimensional spatial degree of freedom of photons.

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

Exploring the Impact of C-Terminal Based Pentapeptides on the Disassembly of Aβ42 Fibrils.

An effective therapeutic strategy to suppress Alzheimer's disease (AD) progression is to disrupt β-sheet rich neurotoxic soluble amyloid-β (Aβ) aggregates. Previously, we identified new pentapeptides (RVVPI and RIAPA) with notably enhanced ability to block Aβ42 aggregation as compared to Aβ42 C-terminal derived peptide RIIGL using integrated computational protocol. In this work, the potential of RIIGL, RVVPI, and RIAPA for the structural destabilization of Aβ42 protofibril was assessed by molecular dynamics (MD) simulations and in vitro studies. The binding free energy analysis depicts that charged residues influence Aβ42 protofibril-pentapeptide interactions. Notably, RVVPI displays a more pronounced destabilization effect than other peptides due to higher conformational fluctuations, and disruption of salt bridge (K28-A42) interactions in Aβ42 protofibril. RVVPI exhibited highest inhibitory activity (Inhibition=66.2 %, IC50=5.57±0.83 μM) against Aβ42 aggregation consistent with computational results. Remarkably, RVVPI displayed ~4.5 fold lower IC50 value as compared to RIIGL. ThT and TEM studies highlighted the enhanced efficiency of RVVPI (62.4 %) in the disassembly of pre-formed Aβ42 fibrils than RIIGL and RIAPA. The combined in silico and in vitro studies identified a new peptide, RVVPI, as an efficient inhibitor of Aβ42 fibrillation and disassembly of Aβ42 aggregates.

A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.

We propose that one of the main hurdles in delivering comprehensively informed care results from the challenges surrounding the extraction, representation, and retention of prior clinical experience and basic medical knowledge, as well as its translation into time- and context-informed actionable interventions. While emerging applications in artificial intelligence-based techniques, for example, large language models, offer impressive pattern association capabilities, they often fall short in producing human-readable explanations crucial to their integration into clinical care. Moreover, they require large well-defined and well-integrated data sets that typically conflict with the availability of such data in all but a few areas of medicine, for example, medical imaging and neuroimaging, noninvasive monitoring of bio-electrical activity, etc. In this chapter, we argue that approximate reasoning rooted in the knowledge that is explainable to the human clinician may offer attractive avenues for the introduction of such knowledge in a systematic way that supports formal retention, sharing, and reuse of new clinical and basic medical experience. We outline a conceptual protocol that targets the use of sparse and disparate data of different types and from different sources, seamlessly drawing on our collective experience and that of others. We illustrate the utility of such an integrative approach by applying the latter to the assessment and reconciliation of data from different experimental models, human and animal, in the example use case of a complex health condition.

Synchronized motion of interface residues for evaluating protein-RNA complex binding affinity: Application to aptamer-mediated inhibition of TDP-43 aggregates.

Investigating the binding between proteins and aptamers, such as peptides or RNA molecules, is of crucial importance both for understanding the molecular mechanisms that regulate cellular activities and for therapeutic applications in several pathologies. Here, a new computational procedure, employing mainly docking, clustering analysis, and molecular dynamics simulations, was designed to estimate the binding affinities between a protein and some RNA aptamers, through the investigation of the dynamical behavior of the predicted molecular complex. Using the state-of-the-art software catRAPID, we computationally designed a set of RNA aptamers interacting with the TAR DNA-binding protein 43 (TDP-43), a protein associated with several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). We thus devised a computational protocol to predict the RNA-protein molecular complex, so that the structural and dynamical behavior of such a complex can be investigated through extensive molecular dynamics simulation. We hypothesized that the coordinated and synchronized motion of the protein-binding residues, when in contact with RNA molecule, is a critical requisite in order to have a stable binding. Indeed, we calculated the motion covariance exhibited by the interface residues during molecular dynamics simulation: we tested the results against experimental measurements of binding affinity (in this case, the dissociation constant) for six RNA molecules, resulting in a linear correlation of about 0.9. Our findings suggest that the synchronized movement of interface residues plays a pivotal role in ensuring the stability within RNA-protein complexes, moreover providing insights into the contribution of each interface residue. This promising pipeline could thus contribute to the design of RNA aptamers interacting with proteins.

Overview of Therapeutic Ultrasound Applications and Safety Considerations: 2024 Update.

A 2012 review of therapeutic ultrasound was published to educate researchers and physicians on potential applications and concerns for unintended bioeffects (doi: 10.7863/jum.2012.31.4.623). This review serves as an update to the parent article, highlighting advances in therapeutic ultrasound over the past 12 years. In addition to general mechanisms for bioeffects produced by therapeutic ultrasound, current applications, and the pre-clinical and clinical stages are outlined. An overview is provided for image guidance methods to monitor and assess treatment progress. Finally, other topics relevant for the translation of therapeutic ultrasound are discussed, including computational modeling, tissue-mimicking phantoms, and quality assurance protocols.