Atom-based Approach Research Articles

An Affordable Topography-Based Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) Scale.

The hydropathy of proteins or quantitative assessment of protein-water interactions has been a topic of interest for decades. Most hydropathy scales use a residue-based or atom-based approach to assign fixed numerical values to the 20 amino acids and categorize them as hydrophilic, hydroneutral, or hydrophobic. These scales overlook the protein's nanoscale topography, such as bumps, crevices, cavities, clefts, pockets, and channels, in calculating the hydropathy of the residues. Some recent studies have included protein topography in determining hydrophobic patches on protein surfaces, but these methods do not provide a hydropathy scale. To overcome the limitations in the existing methods, we have developed a Protocol for Assigning a Residue's Character on the Hydropathy (PARCH) scale that adopts a holistic approach to assigning the hydropathy of a residue. The parch scale evaluates the collective response of the water molecules in the protein's first hydration shell to increasing temperatures. We performed the parch analysis of a set of well-studied proteins that include the following─enzymes, immune proteins, and integral membrane proteins, as well as fungal and virus capsid proteins. Since the parch scale evaluates every residue based on its location, a residue may have very different parch values inside a crevice versus a surface bump. Thus, a residue can have a range of parch values (or hydropathies) dictated by the local geometry. The parch scale calculations are computationally inexpensive and can compare hydropathies of different proteins. The parch analysis can affordably and reliably aid in designing nanostructured surfaces, identifying hydrophilic and hydrophobic patches, and drug discovery.

Second-Generation Enamine-Type Schiff Bases as 2-Amino Acid-Derived Antifungals against Fusarium oxysporum: Microwave-Assisted Synthesis, In Vitro Activity, 3D-QSAR, and In Vivo Effect.

In this manuscript, the synthesis of enamine-type Schiff bases 1-48 derived from the amino acids L-Ala, L-Tyr, and L-Phe was carried out. Their in vitro activity and in vivo protective effect against Fusarium oxysporum were also evaluated through mycelial growth inhibition and disease severity reduction under greenhouse conditions. The in vitro activity of test compounds 1-48 showed half-maximal inhibitory concentrations (IC50) at different levels below the 40 mM range. Deep analysis of the IC50 variations indicated that the size of the substituent on the acetylacetone derivatives and the electronic character on the cyclohexane-3-one fragment influenced the antifungal effect. 3D-QSAR models based on atoms (atom-based approach) were built to establish the structure-activity relationship of the test Schiff bases, showing a good correlation and predictive consistency (R2 > 0.70 and Q2 > 0.60). The respective contour analysis also provided information about the structural requirements for potentiating their antifungal activity. In particular, the amino acid-related fragment and the alkyl ester residue can favor hydrophobic interactions. In contrast, the nitrogen atoms and enamine substituent are favorable regions as H-donating and electron-withdrawing moieties. The most active compounds (40 and 41) protected cape gooseberry plants against F. oxysporum infection (disease severity index < 2), involving adequate physiological parameters (stomatal conductance > 150 mmol/m2s) after 45 days of inoculation. These promising results will allow the design of novel Schiff base-inspired antifungals using 2-amino acids as precursors.

Atom-based sensing technique of microwave electric and magnetic fields via a single rubidium vapor cell.

We present an atom-based approach for determining microwave electric and magnetic fields by using a single rubidium vapor cell in a microwave waveguide. For a 87Rb cascade three-level system employed in our experiment, a weak probe laser driving the lower transition, 5S1/2→5P3/2, is first used to measure the microwave magnetic field based on the atomic Rabi resonance. When a counter-propagating strong coupling laser is subsequently turned on to drive the Rydberg transition, 5P3/2→67D5/2, the same probe laser is then used as a Rydberg electromagnetically induced transparency (EIT) probe to measure the microwave electric field by investigating the resonant microwave dressed Autler-Townes splitting (ATS). By tuning the hyperfine transition frequency of the ground state using an experimentally feasible static magnetic field, we first achieved a measurement of the microwave electric and magnetic field strength at the same microwave frequency of 6.916 GHz. Based on the ideal relationship between the electric and magnetic field components, we obtained the equivalent microwave magnetic fields by fitting the inversion to the measured microwave electric fields, which demonstrated that the results were in agreement with the experimental measurement of the microwave magnetic fields in the same microwave power range. This study provides new experimental evidence for quantum-based microwave measurements of electric and magnetic fields by a single sensor in the same system.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning.

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Machine learning-based modeling of high-pressure phase diagrams: Anomalous melting of Rb.

Modeling of phase diagrams and, in particular, the anomalous re-entrant melting curves of alkali metals is an open challenge for interatomic potentials. Machine learning-based interatomic potentials have shown promise in overcoming this challenge, unlike earlier embedded atom-based approaches. We introduce a relatively simple and inexpensive approach to develop, train, and validate a neural network-based, wide-ranging interatomic potential transferable across both temperature and pressure. This approach is based on training the potential at high pressures only in the liquid phase and on validating its transferability on the relatively easy-to-calculate cold compression curve. Our approach is demonstrated on the phase diagram of Rb for which we reproduce the cold compression curve over the Rb-I (BCC), Rb-II (FCC), and Rb-V (tI4) phases, followed by the high-pressure melting curve including the re-entry after the maximum and then the minimum at the triple liquid-FCC-BCC point. Furthermore, our potential is able to partially capture even the very recently reported liquid-liquid transition in Rb, indicating the utility of machine learning-based potentials.

CReM: chemically reasonable mutations framework for structure generation

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

Theories of Aboutness

ABSTRACTOur topic is the theory of topics (that is, the theory of subject matter). My goal is to clarify and evaluate three competing traditions: what I call the way-based approach, the atom-based approach, and the subject-predicate approach. I develop (defeasible) criteria for adequacy using robust linguistic intuitions that feature prominently in the literature. Then I evaluate the extent to which various existing theories satisfy these constraints. I conclude that recent theories due to Parry, Perry, Lewis, and Yablo do not meet the constraints in total. I then introduce the issue-based theory—a novel and natural entry in the atom-based tradition that meets our constraints. In a coda, I categorize a recent theory from Fine as atom-based, and contrast it to the issue-based theory, concluding that they are evenly matched, relative to our main criteria of adequacy. I offer tentative reasons to nevertheless favour the issue-based theory.

Measuring microwave cavity response using atomic Rabi resonances

In this letter, an atom-based approach for measuring the microwave (MW) cavity response (including cavity frequency and Q-factor) is presented, which utilizes a MW magnetic field detection technique based on atomic Rabi resonances. We first identify the Rabi resonances on seven π transitions in Cs atoms and demonstrate their uses in continuously frequency-tunable field detectors. With the atom-based field detectors, we then indicate the possibility of reconstructing the MW cavity response by measuring the MW frequency-dependent Rabi frequency (i.e., MW field strength) inside the cavity. To demonstrate this approach, we measured the response curves of a 9.2-GHz cavity and a cavity resonating at 8.3 GHz and 9.7 GHz using π transitions and σ transitions, respectively. We compared the results measured by our approach with those measured by Vector Networker Analyzer and obtained good agreement. From such atom-based, SI-traceable measurements, the MW cavity response can be linked directly to the Rabi frequency, which could be referred to an atomic clock.

Direct orientation sampling of diatomic molecules for path integral Monte Carlo calculation of fully quantum virial coefficients

We develop an orientation sampling algorithm for rigid diatomic molecules, which allows direct generation of rings of images used for path-integral calculation of nuclear quantum effects. The algorithm treats the diatomic molecule as two independent atoms as opposed to one (quantum) rigid rotor. Configurations are generated according to a solvable approximate distribution that is corrected via the acceptance decision of the Monte Carlo trial. Unlike alternative methods that treat the systems as a quantum rotor, this atom-based approach is better suited for generalization to multi-atomic (more than two atoms) and flexible molecules. We have applied this algorithm in combination with some of the latest ab initio potentials of rigid H2 to compute fully quantum second virial coefficients, for which we observe excellent agreement with both experimental and simulation data from the literature.

Extended GT-STAF information indices based on Markov approximation models

A series of novel information theory-based molecular parameters derived from the insight of a molecular structure as a chemical communication system were recently presented and usefully employed in QSAR/QSPRs (J. Comp. Chem, 2013, 34, 259; SAR and QSAR in Environ. Res. 2013, 24). This approach permitted the application of Shannon’s source and channel coding entropic measures to a chemical information source comprised of molecular ‘fragments’, using the zero-order Markov approximation model (atom-based approach). This report covers the theoretical aspects of the extensions of this approach to higher-order models, introducing the first, second and generalized-order Markov approximation models.

Log P Estimation of 1,2-Dithiole-3-thiones and 1,2-Dithiole-3-ones: A Comparison of Experimental and Calculative Approaches

To estimate experimental log P values of formerly described 5-formyl- and 5-acyl-dithiole-3-thiones (DTT) and -dithiole-3-ones (DTO) and to check the validity of five log P calculation programs via experimental log P for a database of 68 DTT and DTO. Experimental log P values were measured by means of octanol/water partitioning; for determining solute concentrations in water, RP-HPLC with spectrophotometric detection was used. For calculating log P, the fragmental methods ACD/log P, CLOGP, and KOWWIN, the atom-based approach XLOGP, and the whole-molecule approach QLOGP were applied. Quality of calculations significantly differs depending on the subset under consideration. For database compounds 01-48, comprising alkyl and aryl substitution in 4- and 5-position, the fragmental methods ACD/log P, CLOGP, and KOWWIN perform significantly better than the atom-based approach XLOGP and the whole-molecule method QLOGP. For database compounds 49-68, comprising formyl and acyl substitution in 4- and 5-position, superiority of the whole-molecule method QLOGP over the substructure-based approaches is observed. The strong underestimation of log P for compounds 49-68 probably indicates hidden physicochemical phenomena resulting from the juxtaposition of the acyl and dithiole moieties. All calculation methods included in this study need a thorough refinement to adequately cope with particular solvation behavior suspected to prevail in formyl- or acyl-DTT and DTO, which represent a chemical class of high pharmacological interest.