The success rate of processed predicted models in molecular replacement: implications for experimental phasing in the AlphaFold era.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

Molecular docking analysis of imeglimin and its derivatives with estrogen receptor-alpha.

Estrogen receptor-α (ER- α) is a principal endocrine regulatory protein in breast cancer. The progression of ER-α positive breast cancer is slowed by selective estrogen receptor modulators such as Tamoxifen. But, long term therapy with Tamoxifen leads to resistance. Therefore, it is of interest to document the Molecular docking and pharmacokinetic analysis of imeglimin derivatives with ER-alpha. Among the 166 derivatives of Imeglimin, only five derivatives were shortlisted after toxicity testing. The selected derivatives showed good binding affinity with favorable pharmacokinetic profiles. The selected compounds of Imeglimin were found to possess excellent anticancer potential and could be considered as novel, cost-effective anticancer agents effective against ER positive breast cancer for further investigation.

Geographical and genetic clines in Dracocephalum kotschyi X Dracocephalum oligadenium hybrids: landscape genetics and genocline analyses.

Conservation and management of medicinally important plants are among the necessary tasks all over the world. The genus Dracocephalum (Lamiaceae) contains about 186 perennials, or annual herb species that have been used for their medicinal values in different parts of the world as an antihyperlipidemic, analgesic, antimicrobial, antioxidant, as well as anticancer medicine. Producing detailed data on the genetic structure of these species and their response against climate change and human landscape manipulation can be very important for conservation purposes. Therefore, the present study was performed on six geographical populations of two species in the Dracocephalum genus, namely, Dracocephalum kotschyi, and Dracocephalum oligadenium, as well as their inter-specific hybrid population. We carried out, population genetic study, landscape genetics, species modeling, and genetic cline analyses on these plants. We present here, new findings on the genetic structure of these populations, and provide data on both geographical and genetic clines, as well as morphological clines. We also identified genetic loci that are potentially adaptive to the geographical spatial features and genocide conditions. Different species distribution modeling (SDM) methods, used in this work revealed that bioclimatic variables related to the temperature and moisture, play an important role in Dracocephalum population's geographical distribution within IRAN and that due to the presence of some potentially adaptive genetic loci in the studied plants, they can survive well enough by the year 2050 and under climate change. The findings can be used for the protection of these medicinally important plant.

Advancements in ionic liquid-based corrosion inhibitors for sustainable protection strategies: from experimental to computational insights.

The global corrosion cost is estimated to be around 2.5 trillion USD, which is more than 3 % of the global GDP. Against this background, large efforts have been made to find effective corrosion inhibitors. Ionic liquids (ILs) are nowadays regarded as reliable functional materials and one of the most promising classes of anticorrosion agents. Not only are they efficient in preventing corrosion of iron and other metals, but they are also relatively inexpensive, need no solvents, and are non-toxic to humans This review addresses both experimental and theoretical investigations conducted to IL-based corrosion inhibitors (CIs). It covers various ILs used, synthesis methods, and their performance in diverse corrosive environments. Electrochemical techniques like EIS and potentiodynamic polarization, along with computational approaches including quantum chemical calculations and DFT, provide valuable insights into corrosion inhibition mechanisms and the interactions between anticorrosion agents-surfaces. The synergistic combination of experimental and theoretical approaches enhances our understanding of corrosion inhibition, enabling the design and optimization of effective and sustainable corrosion protection strategies. This review consolidates the existing knowledge on ionic liquid-based corrosion inhibitors, highlights the key findings from both experimental and theoretical investigations, and points out possible directions for further studies in this area.

Stochastic delayed analysis of coronavirus model through efficient computational method.

Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps.

Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment.

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Computational Fluid-Structure Interaction in Microfluidics.

Micro elastofluidics is a transformative branch of microfluidics, leveraging the fluid-structure interaction (FSI) at the microscale to enhance the functionality and efficiency of various microdevices. This review paper elucidates the critical role of advanced computational FSI methods in the field of micro elastofluidics. By focusing on the interplay between fluid mechanics and structural responses, these computational methods facilitate the intricate design and optimisation of microdevices such as microvalves, micropumps, and micromixers, which rely on the precise control of fluidic and structural dynamics. In addition, these computational tools extend to the development of biomedical devices, enabling precise particle manipulation and enhancing therapeutic outcomes in cardiovascular applications. Furthermore, this paper addresses the current challenges in computational FSI and highlights the necessity for further development of tools to tackle complex, time-dependent models under microfluidic environments and varying conditions. Our review highlights the expanding potential of FSI in micro elastofluidics, offering a roadmap for future research and development in this promising area.

Synthesis, quantum chemical calculations, in silico and in vitro bioactivity of a sulfonamide-Schiff base derivative.

The sulfonamide Schiff base compound (E)-4-((4-(dimethylamino)benzylidene)amino)-N-(5-methylisoxazol-3-yl)benzenesulfonamide was successfully prepared and fully characterized. The foremost objective of this study was to explore the molecular geometry of the aforementioned compound and determine its drug likeness characteristics, docking ability as an insulysin inhibitor, anticancer and antioxidant activities. The molecular structure of this compound was optimized using the B3LYP/6-311G+(d,p) level of theory. The compound was completely characterized utilizing both experimental and DFT approaches. Molecular electrostatic potential, frontier molecular orbitals, Fukui function, drug likeness, and in silico molecular docking analyses of this compound were performed. Wave functional properties such as localized orbital locator, electron localization function and non-covalent interactions were also simulated. The compound was screened for anticancer and antioxidant activities using in vitro technique. The observed FT-IR, UV-Vis, and 1H NMR results compared with simulated data and both results were fairly consistent. The experimental and computational spectral findings confirm the formation of the Schiff base compound. Both π-π* and n-π* transitions were observed in both experimental and computational UV-Vis spectra. The examined compound followed to Pfizer, Golden Triangle, GSK, and Lipinski's rules. Consequently, it possesses a more favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile, making it a suitable candidate for non-toxic oral drug use. Moreover, the compound exhibited promising insulysin inhibition activity in an in silico molecular docking. The compound showed in vitro anticancer activity against A549 cancer cells with an IC50 value of 40.89 µg/mL and moderate antioxidant activity.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review.

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Accelerated Discovery of Halide Perovskite Materials via Computational Methods: A Review.

Halide perovskites have gained considerable attention in materials science due to their exceptional optoelectronic properties, including high absorption coefficients, excellent charge-carrier mobilities, and tunable band gaps, which make them highly promising for applications in photovoltaics, light-emitting diodes, synapses, and other optoelectronic devices. However, challenges such as long-term stability and lead toxicity hinder large-scale commercialization. Computational methods have become essential in this field, providing insights into material properties, enabling the efficient screening of large chemical spaces, and accelerating discovery processes through high-throughput screening and machine learning techniques. This review further discusses the role of computational tools in the accelerated discovery of high-performance halide perovskite materials, like the double perovskites A2BX6 and A2BB'X6, zero-dimensional perovskite A3B2X9, and novel halide perovskite ABX6. This review provides significant insights into how computational methods have accelerated the discovery of high-performance halide perovskite. Challenges and future perspectives are also presented to stimulate further research progress.

Ab initio framework for deciphering trade-off relationships in multi-component alloys.

While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use in searching for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a framework to explore Pareto-optimal compositions by integrating advanced ab initio-based techniques into a Bayesian multi-objective optimization workflow, complemented by a simple analytical model providing straightforward analysis of trends. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Properties obtained from ab initio calculations are subsequently used to extend predictions of all relevant material properties to a large class of refractory alloys with the help of the analytical model validated by the data and relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.

Exploring Cannabinoids as Potential Inhibitors of SARS-CoV-2 Papain-like Protease: Insights from Computational Analysis and Molecular Dynamics Simulations.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a global COVID-19 pandemic, challenging healthcare systems worldwide. Effective therapeutic strategies against this novel coronavirus remain limited, underscoring the urgent need for innovative approaches. The present research investigates the potential of cannabis compounds as therapeutic agents against SARS-CoV-2 through their interaction with the virus's papain-like protease (PLpro) protein, a crucial element in viral replication and immune evasion. Computational methods, including molecular docking and molecular dynamics (MD) simulations, were employed to screen cannabis compounds against PLpro and analyze their binding mechanisms and interaction patterns. The results showed cannabinoids with binding affinities ranging from -6.1 kcal/mol to -4.6 kcal/mol, forming interactions with PLpro. Notably, Cannabigerolic and Cannabidiolic acids exhibited strong binding contacts with critical residues in PLpro's active region, indicating their potential as viral replication inhibitors. MD simulations revealed the dynamic behavior of cannabinoid-PLpro complexes, highlighting stable binding conformations and conformational changes over time. These findings shed light on the mechanisms underlying cannabis interaction with SARS-CoV-2 PLpro, aiding in the rational design of antiviral therapies. Future research will focus on experimental validation, optimizing binding affinity and selectivity, and preclinical assessments to develop effective treatments against COVID-19.

In Silico Assessment of Biomolecule Reactivity with Leachables.

Leachables in pharmaceutical products may react with biomolecule active pharmaceutical ingredients (APIs), for example, monoclonal antibodies (mAb), peptides, and ribonucleic acids (RNA), potentially compromising product safety and efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film-forming agents, whereas specific chemical classes included epoxides, acrylates, and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data showed that the sensitivity of an in silico screen using multiple methodologies was 80%-90% and the specificity was 58%-92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modeling and quality risk management.

Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome.

PURPOSE: Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome. METHODS: [18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score. RESULTS: Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001). CONCLUSIONS: Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.

A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.