Molecular Biology Science Research Articles

Novel Vibrational Proteins.

Genetically encoded green fluorescent protein (GFP) and its brighter and redder variants have tremendously revolutionized modern molecular biology and life science by enabling direct visualization of gene regulated protein functions on microscopic and nanoscopic scales. However, the current fluorescent proteins (FPs) only emit a few colors with an emission width of about 30-50 nm. Here, we engineer novel vibrational proteins (VPs) that undergo much finer vibrational transitions and emit rather narrow vibrational spectra (0.1-0.3 nm, roughly 3-10 cm-1). In response to an amber stop codon (UAG), a terminal alkyne bearing an unnatural amino acid (UAA, pEtF) is directly incorporated in place of Tyr64 in the chromophore of pr-Kaede by genetic code expansion. Essentially, the UAA64 further conjugates into a large π system with the contiguous two editable amino acid residues (His63 and Gly65), resulting in a programmable Raman resonance shift of the embedded alkyne. In the proof-of-concept experiment, we constructed a series of novel pEtF-VP mutants and observed fine Raman shifts of the alkynyl group in different chromophores. The genetically encoded novel VPs, could potentially label tens of proteins in the future.

Machine Learning-Driven Prediction of DLD Chip Throughput

Abstract The microfluidic chip technology, capable of manipulating fluids at the micrometer-scale, is increasingly being applied in the fields of cell biology, molecular biology, chemistry, and life sciences. The densely integrated microfluidic chip devices enable high-throughput parallel experiments and integration of various operational units. However, the development of densely integrated microfluidic chips also comes with high demands on driving equipment. Due to manufacturing processes and inherent design limitations, the driving capability of the equipment is restricted. To address potential challenges faced by microfluidic chips in the development towards integrated biological microsystems and to maximize their high-throughput performance, improvements are required not only in selecting appropriate driving equipment but also in design aspects. This study focuses on the DLD chip and delves into the complexity of microfluidic chip design. By combining Bézier curves to characterize arbitrarily shaped micropillars and conducting finite element analysis to compute the pressure field of DLD chips, we explore methods utilizing random forest, XGBoost, LightGBM, and ANN machine learning algorithms to predict the impedance of DLD chips. Our objective is to guide engineers in designing chips with smaller impedance (lower pressure drop) and larger throughput more quickly and efficiently during the design phase. Ultimately, through evaluating the predictive capabilities of the four models on new data, we select the ANN algorithm model to predict the pressure drop under different designs of DLD chips. This offers possibilities for enhancing the efficiency and integration of microfluidic technology in biomedical applications.

Computational tools for plant genomics and breeding.

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Selective Formation of Pd‐DNA Hybrids Using Tailored Palladium‐Mediated Base Pairs: Towards Heteroleptic Pd‐DNA Systems

AbstractThe formation of highly organized metal‐DNA structures has significant implications in bioinorganic chemistry, molecular biology and material science due to their unique properties and potential applications. In this study, we report on the conversion of single‐stranded polydeoxycytidine (dC15) into a Pd‐DNA supramolecular structure using the [Pd(Aqa)] complex (Aqa=8‐amino‐4‐hydroxyquinoline‐2‐carboxylic acid) through a self‐assembly process. The resulting Pd‐DNA assembly closely resembles a natural double helix, with continuous [Pd(Aqa)(C)] (C=cytosine) units serving as palladium‐mediated base pairs, forming interbase hydrogen bonds and intrastrand stacking interactions. Notably, the design of the [Pd(Aqa)] complex favours the interaction with cytosine, distinguishing it from our previously reported [Pd(Cheld)] complex (Cheld=chelidamic acid). This finding opens possibilities for creating heteroleptic Pd‐DNA hybrids where different complexes specifically bind to nucleobases. We confirmed the Pd‐DNA supramolecular structural assembly and selective binding of the complexes using NMR spectroscopy, circular dichroism, mass spectrometry, isothermal titration calorimetry, and DFT calculations.

Selective Formation of Pd-DNA Hybrids Using Tailored Palladium-Mediated Base Pairs: Towards Heteroleptic Pd-DNA Systems.

The formation of highly organized metal-DNA structures has significant implications in bioinorganic chemistry, molecular biology and material science due to their unique properties and potential applications. In this study, we report on the conversion of single-stranded polydeoxycytidine (dC15 ) into a Pd-DNA supramolecular structure using the [Pd(Aqa)] complex (Aqa=8-amino-4-hydroxyquinoline-2-carboxylic acid) through a self-assembly process. The resulting Pd-DNA assembly closely resembles a natural double helix, with continuous [Pd(Aqa)(C)] (C=cytosine) units serving as palladium-mediated base pairs, forming interbase hydrogen bonds and intrastrand stacking interactions. Notably, the design of the [Pd(Aqa)] complex favours the interaction with cytosine, distinguishing it from our previously reported [Pd(Cheld)] complex (Cheld=chelidamic acid). This finding opens possibilities for creating heteroleptic Pd-DNA hybrids where different complexes specifically bind to nucleobases. We confirmed the Pd-DNA supramolecular structural assembly and selective binding of the complexes using NMR spectroscopy, circular dichroism, mass spectrometry, isothermal titration calorimetry, and DFT calculations.

Herman F. Mark: Pioneer in structural chemistry, molecular biology, and polymer science

Herman F. Mark: Pioneer in structural chemistry, molecular biology, and polymer science

Exploring the Complexity of Protein Structure Determination Through X-ray Diffraction

The determination of a protein structure by using X-ray diffraction encompasses a series of sequential steps (including gene identification and cloning, protein expression and purification, crystallization, phasing model building, refinement, and validation), which need the application of several methodologies derived from molecular biology, bioinformatics, and physical sciences. This article thoroughly examines the complicated procedure of elucidating protein structures within plant biology, using X-ray diffraction as the primary methodology. Commencing with the gene identification process and progressing toward crystallography, this article explores the many obstacles and achievements in acquiring diffraction pictures and their subsequent conversion into electron density maps. The ensuing phases of model construction, refinement, and structural validation are thoroughly examined, providing insight into the inherent complexity associated with each stage. The paper also discusses the critical component of understanding the resultant model and scrutinizing its biological significance. By comprehensively examining these stages, this article presents a nuanced comprehension of the intricate procedure in ascertaining protein structures within plant biology. It offers valuable insights into the obstacles encountered and the biological importance of the acquired structural data.

Comparative analysis of computational based motif refinement methods

Finding patterns in DNA sequences, a challenging task in molecular biology and computer science, plays a crucial role in understanding gene expression and transcription regulation. This article explores various computational methods for identifying transcription factor binding sites TFBS and regulatory motifs in DNA sequences. We discuss the significance of these motifs in gene expression and disease susceptibility research. The article categorizes motif discovery algorithms into enumerative, probabilistic, combinatorial, and nature-inspired approaches, highlighting their strengths and limitations. It emphasizes the need for a comprehensive motif discovery tool that can handle diverse motif types, perform global searches efficiently, and reduce user-defined parameters.

Neuropathic pain; what we know and what we should do about it.

Neuropathic pain can result from injury to, or disease of the nervous system. It is notoriously difficult to treat. Peripheral nerve injury promotes Schwann cell activation and invasion of immunocompetent cells into the site of injury, spinal cord and higher sensory structures such as thalamus and cingulate and sensory cortices. Various cytokines, chemokines, growth factors, monoamines and neuropeptides effect two-way signalling between neurons, glia and immune cells. This promotes sustained hyperexcitability and spontaneous activity in primary afferents that is crucial for onset and persistence of pain as well as misprocessing of sensory information in the spinal cord and supraspinal structures. Much of the current understanding of pain aetiology and identification of drug targets derives from studies of the consequences of peripheral nerve injury in rodent models. Although a vast amount of information has been forthcoming, the translation of this information into the clinical arena has been minimal. Few, if any, major therapeutic approaches have appeared since the mid 1990's. This may reflect failure to recognise differences in pain processing in males vs. females, differences in cellular responses to different types of injury and differences in pain processing in humans vs. animals. Basic science and clinical approaches which seek to bridge this knowledge gap include better assessment of pain in animal models, use of pain models which better emulate human disease, and stratification of human pain phenotypes according to quantitative assessment of signs and symptoms of disease. This can lead to more personalized and effective treatments for individual patients. Significance statement: There is an urgent need to find new treatments for neuropathic pain. Although classical animal models have revealed essential features of pain aetiology such as peripheral and central sensitization and some of the molecular and cellular mechanisms involved, they do not adequately model the multiplicity of disease states or injuries that may bring forth neuropathic pain in the clinic. This review seeks to integrate information from the multiplicity of disciplines that seek to understand neuropathic pain; including immunology, cell biology, electrophysiology and biophysics, anatomy, cell biology, neurology, molecular biology, pharmacology and behavioral science. Beyond this, it underlines ongoing refinements in basic science and clinical practice that will engender improved approaches to pain management.

Combatting drought: a multi-dimensional challenge.

Water will be a major limitation to food production in the 21st century, and drought issues already prevail in many parts of the world. Finding solutions to ensure that farmers harvest profitable crops, and secure food supplies for families and feed for animals that will provide for them through to the next season are urgent necessities. The Interdrought community has been addressing this issue for almost 30 years in a series of international conferences, characterized by a multi-disciplinary approach across the domains of molecular biology, physiology, genetics, agronomy, breeding, environmental and social sciences, policy, and systems modeling. This special issue presents papers from the 7th edition of the conference, the first to be held in Africa, that paid special attention to drought in a smallholder context, adding a 'system' dimension to the crop focus from the previous Interdrought events (Varshney et al., 2018; Hammer et al., 2021).

Fungal taxonomy: A puzzle with many missing pieces

Fungi are multifaceted organisms found in almost all ecosystems on Earth, where they establish various types of symbiosis with other living beings. Despite being recognized by humans since ancient times, and the high number of works delving into their biology and ecology, much is still unknown about these organisms. Some criteria classically used for their study are nowadays limited, generating confusion in categorizing them, and even more, when trying to understand their genealogical relationships. To identify species within Fungi, phenotypic characters to date are not sufficient, and to construct a broad phylogeny or a phylogeny of a particular group, there are still gaps affecting the generated trees, making them unstable and easily debated. For health professionals, fungal identification at lower levels such as genus and species, is enough to select the most appropriate therapy for their control, understand the epidemiology of clinical pictures associated, and recognize outbreaks and antimicrobial resistance. However, the taxonomic location within the kingdom, information with apparently little relevance, can allow phylogenetic relationships to be established between fungal taxa, facilitating the understanding of their biology, distribution in nature, and pathogenic potential evolution. Advances in molecular biology and computer science techniques from the last 30 years have led to crucial changes aiming to establish the criteria to define a fungal species, allowing us to reach a kind of stable phylogenetic construction. However, there is still a long way to go, and it requires the joint work of the scientific community at a global level and support for basic research.

Porphyrins – Effective Photocatalysts for Red Light-Induced Functionalizations of Biomolecules

Recently, photoredox catalysis has begun to influence molecular biology and medical sciences for its mild conditions for generating highly reactive species (i.e., radicals), enabling novel and selective functionalisations of biomolecules.[1] In line with this, porphyrins, which can transfer energy (photosensitization) or electrons (photoredox catalysis) when exposure to light, seem to be highly advantageous. Given that their electron absorption shows a characteristic Soret band at 420 nm with a high molar extinction coefficient (105 M-1 cm-1),[2] they have been mainly used in blue-light-induced reactions. These molecules, however, absorb red light (four Q-bands at 518, 553, 592 and 648 nm with molar extinction coefficients of 104 M-1 cm), which has the benefit of low energy, lower health risks,[3] and more in-depth penetration of various media.[4] Fig. 1: Porphyrins in red lightinduced transformationsWe established that due to their diverse photophysical properties, porphyrins promote photoinduced electron transfer events under red light irradiation.[5] They act as effective photooxidants (alkylation of carbonyl compounds, thiol-ne reaction and reductive decarboxylation) and photoreductants (in arylation of heteroarenes, selenylation, thiolation and reduction of nitro compounds). These bioinspired photocatalysts exhibit features that outperform other catalysts operating under red light, as they are really non-toxic and can be applied in biological systems. Thus, we believe that free-radical porphyrins are a valuable contribution to the set of red-light photocatalysts, and this study will lead to more practical biosynthetic applications.

The potential of using non-coding RNAs in forensic science applications.

With the continuous development and integration of molecular biology and forensic science, non-coding RNAs (ncRNAs), especially ncRNAs with regulatory functions such as microRNA, long non-coding RNA, and circular RNA, have recently been actively explored by forensic scholars. In this study, we review the literature on these ncRNAs in various fields of forensic science, including postmortem interval determination, wound age estimation, forensic age assessment, cause of death analysis, and body fluid identification, aiming to evaluate the current research and provide a perspective for future applications.

Global development and future trends of artificial sweetener research based on bibliometrics

Artificial sweeteners have sparked a heated debate worldwide due to their ambiguous impacts on public and environmental health and food safety and quality. Many studies on artificial sweeteners have been conducted; however, none scientometric studies exist in the field. This study aimed to elaborate on the knowledge creation and development of the field of artificial sweeteners and predict the frontiers of knowledge based on bibliometrics. In particular, this study combined VOSviewer, CiteSpace, and Bibliometrix to visualize the mapping of knowledge production, covered 2389 relevant scientific publications (1945–2022), and systematically analyzed articles and reviews (n = 2101). Scientific publications on artificial sweeteners have been growing at an annual rate of 6.28% and globally attracting 7979 contributors. Susan J. Brown with total publications (TP) of 17, average citation per article (AC) of 36.59, and Hirsch (h)-index of 12 and Robert F. Margolskee (TP = 12; AC = 2046; h-index = 11) were the most influential scholars. This field was clustered into four groups: eco-environment and toxicology, physicochemical mechanisms, public health and risks, and nutrition metabolism. The publications about environmental issues, in particular, “surface water,” were most intensive during the last five years (2018–2022). Artificial sweeteners are gaining importance in the monitoring and assessment of environmental and public health. Results of the dual-map overlay showed that the future research frontiers tilt toward molecular biology, immunology, veterinary and animal sciences, and medicine. Findings of this study are conducive to identifying knowledge gaps and future research directions for scholars.

Entropy structural characterization of zeolites BCT and DFT with bond-wise scaled comparison

Entropy of a connected network is a quantitative measure from information theory that has triggered a plethora of research domains in molecular chemistry, biological sciences and computer programming due to its inherent capacity to explore the structural characteristics of complex molecular frameworks that have low structural symmetry as well as high diversity. The analysis of the structural order is greatly simplified through the topological indices based graph entropy metrics, which are then utilized to predict the structural features of molecular frameworks. This predictability has not only revolutionized the study of zeolitic frameworks but has also given rise to new generations of frameworks. We make a comparative study of two versatile framework topologies namely zeolites BCT and DFT, which have been widely utilized to create a new generation of frameworks known as metal organic frameworks. We discuss bond-additive topological indices and compute entropy measure descriptors for zeolites BCT and DFT using degree and degree-sum parameters. In addition, we perform bond-wise scaled comparative analysis between BCT and DFT which shows that zeolite BCT has greater entropy values compared to zeolite DFT.

Machine Learning Methods for Small Data Challenges in Molecular Science.

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

A bibliometric profile of optogenetics: quantitative and qualitative analyses.

Optogenetics is a rapidly developing field combining optics and genetics, with promising applications in neuroscience and beyond. However, there is currently a lack of bibliometric analyses examining publications in this area. Publications on optogenetics were gathered from the Web of Science Core Collection Database. A quantitative analysis was conducted to gain insights into the annual scientific output, and distribution of authors, journals, subject categories, countries, and institutions. Additionally, qualitative analysis, such as co-occurrence network analysis, thematic analysis, and theme evolution, were performed to identify the main areas and trends of optogenetics articles. A total of 6,824 publications were included for analysis. The number of articles has rapidly grown since 2010, with an annual growth rate of 52.82%. Deisseroth K, Boyden ES, and Hegemann P were the most prolific contributors to the field. The United States contributed the most articles (3,051 articles), followed by China (623 articles). A majority of optogenetics-related articles are published in high-quality journals, including NATURE, SCIENCE, and CELL. These articles mainly belong to four subjects: neurosciences, biochemistry and molecular biology, neuroimaging, and materials science. Co-occurrence keyword network analysis identified three clusters: optogenetic components and techniques, optogenetics and neural circuitry, optogenetics and disease. The results suggest that optogenetics research is flourishing, focusing on optogenetic techniques and their applications in neural circuitry exploration and disease intervention. Optogenetics is expected to remain a hot topic in various fields in the future.

Impact of carbocyclic sugar-based myo-inositol on conventional surfactants

Surfactants and inositol are examples of eco-friendly components that seem to be sustainable in chemistry. The hydrophilic portion of myo-inositol (MI) may be effective in increasing the superficial activity of the micellar systems. It is possible to modify and enhance the aggregation properties of surfactants by selecting suitable additives such as MI. The impact of carbocyclic sugar-based MI on conventional surfactants such as Triton X-100 (TX-100), cetyltrimethylammonium bromide (CTAB), and sodium dodecyl sulfate (SDS) was performed by electrical conductivity, viscometer, colorimetry, stalagmometer, dynamic light scattering (DLS) and FTIR methods. The main focus of this paper was to determine the critical micelle concentration (CMC), hydrodynamic radii (Rh), zeta potential (ζ), various interfacial parameters viz., the surface tension at CMC (γCMC), the surface pressure at CMC (πCMC), the efficiency of adsorption pC20, the minimum surface area per molecule (Amin), the maximum surface excess concentration (Гmax), and thermodynamic parameters i.e., the standard Gibbs free energy of adsorption (ΔG°ads), the standard Gibbs free energy of transfer (ΔG°trans), the standard Gibbs free energy of air–water interface (ΔGsmin), the standard Gibbs free energy of micellization (ΔGom), the standard Gibbs free energy of micellization per alkyl tail (ΔG°tail). A significant decrease in the CMC values of all conventional surfactants was reported with the addition of different concentrations of MI (mM) and its conformational study by colorimetric method. The higher negative values of ΔG°m and ΔG°ads have been shown the micellization and adsorption properties became more spontaneous. Relative viscosity-based rheological behaviour was calculated for various surfactant-MI systems. The DLS and FTIR spectra were used to reveal the improved micellization behaviour of conventional surfactant-MI systems. The FTIR data showed that the CTAB-MI and SDS-MI systems have significantly influenced the stretching frequency as compared to the TX-100-MI system. The present works will apply to drug delivery, agrochemical, molecular biology, and pharmaceutical sciences.

Centella asiatica (L.) Urb: A comprehensive bibliometric analysis of published studies between 1857 and 2022

Centella asiatica (L.) Urban (C. asiatica) is a medicinal plant that generates terpenes, phenols, vitamins, minerals, polyacetylene and fatty acids among other phyto-constituents. This review focuses on the bibliometric analysis of 1164 documents on C. asiatica that were retrieved from the Scopus database. Harzing Publish or Perish and VOSviewer were used for citation and network analyses. According to the bibliometric analysis, publications are on the rise, particularly in the fields of pharmacology, toxicology and pharmaceutics, medicine, biochemistry, genetics and molecular biology, agricultural and biological sciences, and chemistry. "Phytoremediation", "secondary metabolites", "Andrographis paniculata", and "cognitive impairment" are emerging areas for C. asiatica research. However, currently there is a lack of international collaboration in C. asiatica research among contributing countries. Researchers can utilise the findings cited in this reiew to locate potential collaborators, top authors, countries and documents.

A scoping review of the additive manufacturing of mandibular implants

Bone disease and fractures are among the health issues that are becoming more prevalent year after year. A mandibular disorder is caused by an accident or disease to the largest and strongest bone in the human face. Because the natural healing process of mandibular bones takes a long time, a bone grafting procedure is used to speed up the patient’s recovery. Due to the limitations of bone grafting processes such as autographs, allographs, and xenografts, bone replacement is being developed using biomaterials via 3D printing. The purpose of the review was to evaluate research on the use of 3D printing in the replacement of mandible bones. The search algorithm found as many as 2,941 articles at the start of the search and 123 articles after initial selection. Up to February 2022, the Scopus electronic database was used to conduct the literature search. This research includes publications that employ 3D printers, additive manufacturing, or finite element analysis to build or analyze mandibular implants. Paper topics in engineering, materials science, biochemistry, genetics, molecular biology, medicine, dentistry, chemical engineering, and computer science are included in this study. Papers in physics, astronomy, and energy, book chapter document types, papers reviews, and documents in languages other than English were excluded from this study. After an initial screening that included the year, publication stage, source type, and language, as many as 70 articles were obtained, and after filtering titles and abstracts obtained 55 articles. After the full-text selection was obtained, 32 articles were included in this review. Some articles were unacceptable because the topics discussed were unrelated to mandibular bone scaffolds. As a result, the field of additive manufacturing for the repair and reconstruction of mandibular defects necessitates the development of novel tools and methodologies. A customized biological scaffold can be created using an appropriate 3D printing process based on the characteristics of various mandibular defects, allowing it to be perfectly matched to the defect region and reducing stress, thereby improving the scaffold’s healing function.