Fragment Pairs Research Articles

Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins.

Fission fragment spectroscopy of 235U(nth,f)

Simultaneous measurements of the relative fission fragment charge and mass yield distributions have been performed for the even-even fission fragments produced from the reaction, 235U(nth,f). The measurements have been carried out using the conventional fission fragment spectroscopic technique. The extracted results are interpreted on the basis of the Multi-Modal Random Neck Rupture Model (MM-RNRM). The results from the analysis bring out the necessary experimental evidence for the influence of the deformed proton shell closures at Z = 52 and ∼ 56 along with the neutron shell closures at N = 82 (spherical) and 88 (deformed) in controlling the respective S1 and S2 fission modes occurring in the heavier group of asymmetric fission fragments. The new findings from the present investigation provide the crucial inputs for understanding the features of different fission modes that persist in the low-energy fission dynamics of the lighter actinides. The pair wise neutron multiplicity distribution profiles for the five correlated fission fragment pairs have been presented, and the corresponding extracted neutron multiplicity values are also reported.

CLigOpt: controllable ligand design through target-specific optimization.

A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design. We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds. The source code is available via https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Design-through-Target-Specific-Optimisation.

Regional Active Transcription Associates with Homoeologous Exchange Breakpoints in Synthetic Brassica Tetraploids.

Polyploidization plays a crucial role in plant evolution and is becoming increasingly important in breeding. Structural variations and epigenomic repatterning have been observed in synthetic polyploidizations. However, the mechanisms underlying the occurrence and their effects on gene expression and phenotype remain unknown. Here, we investigated genome-wide large deletion/duplication regions (DelDups) and genomic methylation dynamics in leaf organs of progeny from the first eight generations of synthetic tetraploids derived from Chinese cabbage (Brassica rapa L. ssp. pekinensis) and cabbage (Brassica oleracea L. var. capitata). One- or two-copy DelDups, with a mean size of 5.70 Mb (400 kb - 65.85 Mb), occurred from the first generation of selfing and thereafter. The duplication of a fragment in one subgenome consistently coincided with the deletion of its syntenic fragment in the other subgenome, and vice versa, indicating that these DelDups were generated by homoeologous exchanges (HEs). Interestingly, the larger the genomic syntenic region, the higher the frequency of DelDups, further suggesting that the pairing of large homoeologous fragments is crucial for HEs. Moreover, we found that the active transcription of continuously distributed genes in local regions is positively associated with the occurrence of HE breakpoints. In addition, the expression of genes within DelDups exhibited a dosage effect, and plants with extra parental genomic fragments generally displayed phenotypes biased towards the corresponding parent. Genome-wide methylation fluctuated remarkably, which did not clearly affect gene expression on a large scale. Our findings provide insights into the early evolution of polyploid genomes, offering valuable knowledge for polyploidization-based breeding.

Ribozyme-Mediated Gene-Fragment Complementation for Nondestructive Reporting of DNA Transfer within Soil.

Enzymes that produce volatile metabolites can be coded into genetic circuits to report nondisruptively on microbial behaviors in hard-to-image soils. However, these enzyme reporters remain challenging to apply in gene transfer studies due to leaky off states that can lead to false positives. To overcome this problem, we designed a reporter that uses ribozyme-mediated gene-fragment complementation of a methyl halide transferase (MHT) to regulate the synthesis of methyl halide gases. We split the mht gene into two nonfunctional fragments and attached these to a pair of splicing ribozyme fragments. While the individual mht-ribozyme fragments did not produce methyl halides when transcribed alone in Escherichia coli, coexpression resulted in a spliced transcript that translated the MHT reporter. When cells containing one mht-ribozyme fragment transcribed from a mobile plasmid were mixed with cells that transcribed the second mht-ribozyme fragment, methyl halides were only detected following rare conjugation events. When conjugation was performed in soil, it led to a 16-fold increase in methyl halides in the soil headspace. These findings show how ribozyme-mediated gene-fragment complementation can achieve tight control of protein reporter production, a level of control that will be critical for monitoring the effects of soil conditions on gene transfer and the fidelity of biocontainment measures developed for environmental applications.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

Delayed fragmentation of isolated nucleobases induced by MeV ions.

We evaluated the dissociation of isolated gas-phase nucleobase molecules induced by mega electron volt (MeV)-energy ions to gain fundamental insights into the reactions of nucleobases upon fast ion irradiation. We studied five nucleobase molecules-adenine, guanine, cytosine, thymine, and uracil-as gas-phase targets. We compared the fragmentation patterns obtained from carbon ion impacts with those obtained from proton impacts to clarify the effect of heavy ion irradiation. We also compared the results with electron impact and photoionization results. In addition, we identified several delayed fragmentation pathways by analyzing the correlation between fragment pairs generated from singly and doubly charged intermediate ions. To determine the lifetimes of delayed fragmentation from singly charged intermediate ions, we evaluated the detection efficiencies of the microchannel plate detector for the neutral fragment HCN as a function of kinetic energy using a new methodology. As the first demonstration of this method, we estimated the lifetimes of C5H5N5+ generated by 1.2-MeV C+ and 0.5-MeV H+ collisions to be 0.87 ± 0.43 and 0.67 ± 0.09 µs, respectively. These lifetimes were approximately one order of magnitude longer than those of the doubly charged intermediate ion C5H5N52+.

Fragments maintain similar herpetofauna and small mammal richness and diversity to continuous habitat, but community composition and traits differ

ContextHuman disturbance has transformed ecosystems globally, yet studies of the ecological impact of landscape modification are often confounded. Non-random patterns of land clearing cause differing vegetation types and soil productivity between fragments in modified landscapes and reference areas—like national parks—with which they are compared.ObjectivesWe sought to explore the influence of land modification on herpetofauna and small mammal communities using multiple biodiversity measures—species richness and diversity, individual species abundance, and community composition. We also aimed to investigate the role of traits such as diet, habitat breadth, and litter size in moderating species responses to land modification.MethodsWe established 100 sampling sites to survey herpetofauna and small mammals in 11 fragments in an agricultural landscape compared to 11 ecologically equivalent ‘pseudo-fragments’ in a nearby national park in south-eastern Australia. We selected pairs of fragments and pseudo-fragments of the same size and vegetation type, and used identical survey methods to sample pairs simultaneously, thereby controlling for numerous confounding factors, such as differing vegetation type, weather, and survey effort.ResultsSpecies richness and diversity were similar between fragments and pseudo-fragments. Despite this, we found community composition differed markedly—driven by the varying responses of individual species—indicating a shift in fauna communities associated with land modification. Fossorial habit, omnivorous diet, and broad habitat requirements led to higher abundance in fragments whilst arboreality, carnivorous diet, and narrow habitat requirements led to higher abundance in pseudo-fragments.ConclusionsAlthough fragments hold similar numbers of species to continuous areas, they contain distinct and novel communities, and sustain high abundances of some species. These diverse communities are dominated by native species, including threatened species, and their distinctive composition is shaped by traits conducive to persistence amidst land modification. These novel communities may provide a reservoir of resilience in the face of environmental change and should be viewed as complementary to conservation areas.

Rapid Discovery of Antimicrobial and Antimalarial Agents from Natural Product Fragments

Fragment-based drug discovery (FBDD) focuses on small compounds, known as fragments, typically with a molecular weight of less than 300 Da. This study highlights the benefits of employing a pure natural product library for FBDD, contrasting with the predominant use of synthetic libraries. Practical methods for rapidly constructing such libraries from crude extracts were demonstrated across various plant and microbial samples. Twenty-nine (29) natural product fragments, including a new compound (20), were identified. Antimicrobial activities were assessed for a subset of the isolated compounds, revealing potent fragments (MICs 4–8 μg/mL) against Mycobacterium bovis bacille Calmette-Guérin (BCG), Staphylococcus aureus (SA), and methicillin-resistant S. aureus (MRSA). Furthermore, a native mass spectrometry technique was introduced to rapidly identify non-competitive fragments against malarial proteins. As a result, two pairs of non-competitive fragments, lepiotin C (31) and 7-amino deacetoxy cephalosporanic acid (32) binding to dynein light chain 1, methyl gallate (33) and β-santanin (34) binding to dUTPase, were identified, serving as promising starting points for developing potent malarial protein inhibitors.

Fission valleys and favored isotopes emitted from 310\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{310}$$\\end{document}126

The decay channels for the superheavy nucleus 310\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{310}$$\\end{document}126 are studied under the binary macroscopic–microscopic method. The macroscopic part is calculated as the heavy-light interacting charged liquid drops, linked through the finite range Yukawa-plus-exponential force. The microscopic energy is calculated based on the spheroidally deformed two-center shell model. The proton and neutron single-particle levels are introduced in the binary Strutinsky procedure. The resulting shell corrections are added to the macroscopic part to obtain the fission barriers. The dynamical part follows by introducing the Werner–Wheeler inertia. The final lifetimes are calculated for all possible isotopic channels around the most favored heavy-light fragment pairs.

Encoding the space of protein-protein binding interfaces by artificial intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

Equivariant 3D-conditional diffusion model for molecular linker design

AbstractFragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant three-dimensional conditional diffusion model for molecular linker design. Given a set of disconnected fragments, our model places missing atoms in between and designs a molecule incorporating all the initial fragments. Unlike previous approaches that are only able to connect pairs of molecular fragments, our method can link an arbitrary number of fragments. Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments. We demonstrate that DiffLinker outperforms other methods on the standard datasets, generating more diverse and synthetically accessible molecules. We experimentally test our method in real-world applications, showing that it can successfully generate valid linkers conditioned on target protein pockets.

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems.

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree-Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. The accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, Rcut. Rcut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small Rcut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

Applying machine learning methods to identify argumentative connections in scientific communication texts

The paper presents the results of experiments to assess the machine learning methods applicability for solving the problem of identifying argumentative connections in scientific communication texts. Argumentative connection is understood as a relationship that connects the premise and the conclusion of a typical reasoning or an argument used by the author to persuade the readers. To assess the quality, the characteristics of accuracy, completeness and F-measure were used obtained when solving the problem of recognizing argumentative connections between adjacent text fragments of two types: sentences and clauses. The basis of the experiment was a Russian-language corpus of texts from the field of scientific communication with arguments marked up by linguistic experts. For markup, the ArgNetBank Studio tool was used, which allows creating collections of texts with detailed argumentation markup. Data sets for machine learning were built on the basis of labeled texts, in which the ratio of pairs of text fragments (sentences or clauses) connected and non-connected by argumentative relationships was 1 to 3. To improve the quality of model training, the sets were balanced in two ways. In the first case, a balance was achieved due to the fact that an equal number of pairs of both types were selected from each text; in the second, pairs were duplicated. Using the obtained data sets, experiments were carried out on linking text fragments using different types of machine learning methods. The range of changes in quality assessments when recognizing related fragments depending on their share in the training and test collections was experimentally determined. It has been established that, within the framework of the existing imbalance in real collections, the values of quality assessments can vary within 40–50%. The novelty of the work lies in the study of the range of possible discrepancies in quality assessments when applying different machine learning methods on balanced and unbalanced training and test collections in Russian-language material.

GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

AbstractCode clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.

Engineering highly stable variants of Corynactis californica green fluorescent proteins.

Fluorescent proteins (FPs) are versatile biomarkers that facilitate effective detection and tracking of macromolecules of interest in real time. Engineered FPs such as superfolder green fluorescent protein (sfGFP) and superfolder Cherry (sfCherry) have exceptional refolding capability capable of delivering fluorescent readout in harsh environments where most proteins lose their native functions. Our recent work on the development of a split FP from a species of strawberry anemone, Corynactis californica, delivered pairs of fragments with up to threefold faster complementation than split GFP. We present the biophysical, biochemical, and structural characteristics of five full-length variants derived from these split C. californica GFP (ccGFP). These ccGFP variants are more tolerant under chemical denaturation with up to 8 kcal/mol lower unfolding free energy than that of the sfGFP. It is likely that some of these ccGFP variants could be suitable as biomarkers under more adverse environments where sfGFP fails to survive. A structural analysis suggests explanations of the variations in stabilities among the ccGFP variants.

Programmable RNA base editing with photoactivatable CRISPR-Cas13

CRISPR-Cas13 is widely used for programmable RNA interference, imaging, and editing. In this study, we develop a light-inducible Cas13 system called paCas13 by fusing Magnet with fragment pairs. The most effective split site, N351/C350, was identified and found to exhibit a low background and high inducibility. We observed significant light-induced perturbation of endogenous transcripts by paCas13. We further present a light-inducible base-editing system, herein called the padCas13 editor, by fusing ADAR2 to catalytically inactive paCas13 fragments. The padCas13 editor enabled reversible RNA editing under light and was effective in editing A-to-I and C-to-U RNA bases, targeting disease-relevant transcripts, and fine-tuning endogenous transcripts in mammalian cells in vitro. The padCas13 editor was also used to adjust post-translational modifications and demonstrated the ability to activate target transcripts in a mouse model in vivo. We therefore present a light-inducible RNA-modulating technique based on CRISPR-Cas13 that enables target RNAs to be diversely manipulated in vitro and in vivo, including through RNA degradation and base editing. The approach using the paCas13 system can be broadly applicable to manipulating RNA in various disease states and physiological processes, offering potential additional avenues for research and therapeutic development.

Irreversible light-activated SpyLigation mediates split-protein assembly in 4D.

The conditional assembly of split-protein pairs to modulate biological activity is commonly achieved by fusing split-protein fragments to dimerizing components that bring inactive pairs into close proximity in response to an exogenous trigger. However, current methods lack full spatial and temporal control over reconstitution, require sustained activation and lack specificity. Here light-activated SpyLigation (LASL), based on the photoregulation of the covalent SpyTag (ST)/SpyCatcher (SC) peptide-protein reaction, assembles nonfunctional split fragment pairs rapidly and irreversibly in solution, in engineered biomaterials and intracellularly. LASL introduces an ortho-nitrobenzyl(oNB)-caged lysine into SC's reactive site to generate a photoactivatable SC (pSC). Split-protein pairs of interest fused to pSC and ST are conditionally assembled via near-ultraviolet or pulsed near-infrared irradiation, as the uncaged SC can react with ST to ligate appended fragments. We describe procedures for the efficient synthesis of the photocaged amino acid that is incorporated within pSC (<5 days) as well as the design and cloning of LASL plasmids (1-4 days) for recombinant protein expression in either Escherichia coli (5-6 days) or mammalian cells (4-6 days), which require some prior expertise in protein engineering. We provide a chemoenzymatic scheme for appending bioorthogonal reactive handles onto E. coli-purified pSC protein (<4 days) that permits LASL component incorporation and patterned protein activation within many common biomaterial platforms. Given that LASL is irreversible, the photolithographic patterning procedures are fast and do not require sustained light exposure. Overall, LASL can be used to interrogate and modulate cell signaling in various settings.

Impact of nuclear structure of correlated pairs of fission fragments in mass distribution spectra of heavy-ion fusion–fission reactions

Impact of nuclear structure of correlated pairs of fission fragments in mass distribution spectra of heavy-ion fusion–fission reactions

Synthesis, crystal structure investigation, and theoretical approaches to discover potential 6-bromo-3-cyanocoumarin as a potent inhibitor MetAP (methionine aminopeptidase) 2

It has been ascertained that trifluoroacetic acid accelerates the Knoevenagel condensation reaction of 5-bromosalicylaldehyde and ethyl ester of cyanoacetic acid. As a result, the 6-bromo-3-cyanocoumarin compound has been synthesized and the structure was eventually confirmed by X-ray analysis. Based on the structure-activity relationship and literature survey we found this compound has a potent inhibitor for MetAP (methionine aminopeptidase) and further we have studied molecular docking to understand the binding interactions and also performed ADME/T studies for this compound. The surface reactivity and intermolecular interactions of the studied compound were investigated by Hirshfeld surface analysis. Energy framework analysis shows the interaction between pair of fragments with neighbor molecules. Furthermore, the DFT study is performed to investigate structural reactivity and stability at B3LY/6-311+G(d,p) functional. The decreased HOMO-LUMO gap (3.97 eV) indicates its higher reactivity and soft nature. Molecular electrostatic potential (MESP) analysis and NBO charges indicate the structure's reactive site and charge transfer. Excited analysis by using the TD-DFT was performed to get excitation energy (ΔE), absorbance, and oscillator strength (fo) of crucial transition. Theoretically predicted values of polarizability (αo) and hyperpolarizability (βo) suggest their excellent optical and Nnar (NLO) properties.