Regulation and function of endoplasmic reticulum autophagy in neurodegenerative diseases

The endoplasmic reticulum, a key cellular organelle, regulates a wide variety of cellular activities. Endoplasmic reticulum autophagy, one of the quality control systems of the endoplasmic reticulum, plays a pivotal role in maintaining endoplasmic reticulum homeostasis by controlling endoplasmic reticulum turnover, remodeling, and proteostasis. In this review, we briefly describe the endoplasmic reticulum quality control system, and subsequently focus on the role of endoplasmic reticulum autophagy, emphasizing the spatial and temporal mechanisms underlying the regulation of endoplasmic reticulum autophagy according to cellular requirements. We also summarize the evidence relating to how defective or abnormal endoplasmic reticulum autophagy contributes to the pathogenesis of neurodegenerative diseases. In summary, this review highlights the mechanisms associated with the regulation of endoplasmic reticulum autophagy and how they influence the pathophysiology of degenerative nerve disorders. This review would help researchers to understand the roles and regulatory mechanisms of endoplasmic reticulum-phagy in neurodegenerative disorders.

Cryo-Electron Microscopy in the Study of Antiviral Innate Immunity

Cryo-electron microscopy is a powerful methodology in structural biology and has been broadly used in high-resolution structure determination for challenging samples, which are not readily available for traditional techniques. In particular, the strength of super macro-complexes and the lack of a need for crystals for cryo-EM make this technique feasible for the structural study of complexes involved in antiviral innate immunity. This chapter presents detailed information and experimental procedures of Cryo-EM for determining the structures of the complexes using STING as an example. The procedures included a sample quality check, high-resolution data acquisition, and image processing for Cryo-EM 3D structure determination.

Yeast Two-Hybrid Assay for Investigating Antiviral Innate Immunity

Yeast two-hybrid (YTH) technology is a powerful tool for studying protein interactions and has been widely used in various fields of molecular biology, including the study of antiviral innate immunity. This chapter presents detailed information and experimental procedures for identifying virus-host protein interactions involved in immune regulation using yeast two-hybrid technology.

Elucidating structural variability in p53 conformers using combinatorial refinement strategies and molecular dynamics

Low molecular weight proteins and protein assemblies can now be investigated using cryo-electron microscopy (EM) as a complement to traditional structural biology techniques. It is important, however, to not lose sight of the dynamic information inherent in macromolecules that give rise to their exquisite functionality. As computational methods continue to advance the field of biomedical imaging, so must strategies to resolve the minute details of disease-related entities. Here, we employed combinatorial modeling approaches to assess flexible properties among low molecular weight proteins (~100 kDa or less). Through a blend of rigid body refinement and simulated annealing, we determined new hidden conformations for wild type p53 monomer and dimer forms. Structures for both states converged to yield new conformers, each revealing good stereochemistry and dynamic information about the protein. Based on these insights, we identified fluid parts of p53 that complement the stable central core of the protein responsible for engaging DNA. Molecular dynamics simulations corroborated the modeling results and helped pinpoint the more flexible residues in wild type p53. Overall, the new computational methods may be used to shed light on other small protein features in a vast ensemble of structural data that cannot be easily delineated by other algorithms.

Characterization of neutralizing chimeric heavy-chain antibodies against tetanus toxin

Tetanus toxin (TeNT) is one of the most toxic proteins. Neutralizing antibodies against TeNT are effective in prevention and treatment. In this study, 14 anti-tetanus nanobodies were obtained from a phage display nanobody library by immunizing a camel with the C-terminal receptor-binding domain of TeNT (TeNT-Hc) as the antigen. After fusion with the human Fc fragment, 11 chimeric heavy-chain antibodies demonstrated nanomolar binding toward TeNT-Hc. The results of toxin neutralization experiments showed that T83-7, T83-8, and T83-13 completely protected mice against 20 × the median lethal dose (LD50) at a low concentration. The neutralizing potency of T83-7, T83-8, and T83-13 against TeNT is 0.4 IU/mg, 0.4 IU/mg and 0.2 IU/mg, respectively. In the prophylactic setting, we found that 5 mg/kg of T83-13 provided the mice with full protection from tetanus, even when they were injected 14 days before exposure to 20 × LD50 TeNT. T83-7 and T83-8 were less effective, being fully protective only when challenged 7 or 10 days before exposure, respectively. In the therapeutic setting, 12 h after exposure to TeNT, 1 ~ 5 mg/kg of T83-7, and T83-8 could provide complete protection for mice against 5 × LD50 TeNT, while 1 mg/kg T83-13 could provide complete protection 24 h after exposure to 5 × LD50 TeNT. Our results suggested that these antibodies represent prophylactic and therapeutic activities against TeNT in a mouse model. The T83-7, T83-8, and T83-13 could form the basis for the subsequent development of drugs to treat TeNT toxicity.

Maintenance of stem cell self-renewal by sex chromosomal zinc-finger transcription factors

In this Editorial review, we would like to focus on a very recent discovery showing the global autosomal gene regulation by Y- and inactivated X-chromosomal transcription factors, zinc finger gene on the Y chromosome (ZFY) and zinc finger protein X-linked (ZFX). ZFX and ZFY are both zinc-finger proteins that encode general transcription factors abundant in hematopoietic and embryonic stem cells. Although both proteins are homologs, interestingly, the regulation of self-renewal by these transcriptional factors is almost exclusive to ZFX. This fact implies that there are some differential roles between ZFX and ZFY in regulating the maintenance of self-renewal activity in stem cells. Besides the maintenance of stemness, ZFX overexpression or mutations may be linked to certain cancers. Although cancers and stem cells are double-edged swords, there is no study showing the link between ZFX activity and the telomere. Thus, stemness or cancers with ZFX may be linked to other molecules, such as Oct4, Sox2, Klf4, and others. Based on very recent studies and a few lines of evidence in the past decade, it appears that the ZFX is linked to the canonical Wnt signaling, which is one possible mechanism to explain the role of ZFX in the self-renewal of stem cells.

Molecular mechanisms behind BRACO19 and human telomeric G-quadruplex interaction

Human telomeres (HTs) can form DNA G-quadruplex (G4), an attractive target for anticancer and antiviral drugs. HT-G4s exhibit inherent structural polymorphism, posing challenges for understanding their specific recognition by ligands. Here, we aim to explore the impact of different topologies within a small segment of the HT (Tel22) on its interaction with BRACO19, a rationally designed G4 ligand with high quadruplex affinity, already employed in in-vivo treatments. Our multi-technique approach is based on the combined use of a set of contactless spectroscopic tools. Circular dichroism and UV resonance Raman spectroscopy probe ligand-induced conformational changes in the G4 sequence, while UV-visible absorption, coupled with steady-state fluorescence spectroscopy, provides further insights into the electronic features of the complex, exploiting the photoresponsive properties of BRACO19. Overall, we find that modifying the topology of the unbound Tel22 through cations (K+ or Na+), serves as a critical determinant for ligand interactions and binding modes, thus influencing the HT-G4's assembly capabilities. Furthermore, we show how fluorescence serves as a valuable probe for recognizing cation-driven multimeric structures, which may be present in living organisms, giving rise to pathological forms.

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

Emergent microenvironments of nucleoli

In higher eukaryotes, the nucleolus harbors at least three sub-phases that facilitate multiple functionalities including ribosome biogenesis. The three prominent coexisting sub-phases are the fibrillar center (FC), the dense fibrillar component (DFC), and the granular component (GC). Here, we review recent efforts in profiling sub-phase compositions that shed light on the types of physicochemical properties that emerge from compositional biases and territorial organization of specific types of macromolecules. We highlight roles played by molecular grammars which refers to protein sequence features including the substrate binding domains, the sequence features of intrinsically disordered regions, and the multivalence of these distinct types of domains / regions. We introduce the concept of a barcode of emergent physicochemical properties of nucleoli. Although our knowledge of the full barcode remains incomplete, we hope that the concept prompts investigations into undiscovered emergent properties and engenders an appreciation for how and why unique microenvironments control biochemical reactions.

Heterologous biosynthesis of myxobacterial lanthipeptides melittapeptins

The myxobacteria are an attractive bioresource for bioactive compounds since the large size genome contains many biosynthetic gene clusters of secondary metabolites. The genome of the myxobacterium Melittangium boletus contains three biosynthetic gene clusters for lanthipeptide production. One of the gene clusters includes genes coding lanthipeptide precursor (melA), class II lanthipeptide synthetase (melM), and transporter (melT). The amino acid sequence of melA indicated similarity with that of known lanthipeptides mersacidin and lichenicidin A1 by the alignment. To perform heterologous production of new lanthipeptides, the expression vector containing the essential genes (melA and melM) was constructed by utilizing codon-optimized synthetic genes. The co-expression of two genes in the host bacterial cells of Escherichia coli BL21 (DE3) afforded new lanthipeptides named melittapeptins A-C. The structures of melittapeptins A-C including lanthionine/methyllanthionine bridge pattern were proposed based on protease digestion and MS/MS experiments. The native strain of M. boletus did not produce melittapeptins A-C, so heterologous production using the biosynthetic gene cluster was effective in obtaining the lanthipeptides. Melittapeptins A-C showed specific and potent antibacterial activity to the Gram-positive bacterium Micrococcus luteus. To the best of our knowledge, this is the first report of antibacterial lanthipeptides derived from myxobacterial origin. KEY POINTS: • New lanthipeptides melittapeptins were heterologously produced in Escherichia coli. • Melittapeptins showed specific antibacterial activity against Micrococcus luteus. • Melittapeptins were the first antibacterial lanthipeptides of myxobacterial origin.

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

The forecasting power of the mucin-microbiome interplay in livestock respiratory diseases

Complex respiratory diseases are a significant challenge for the livestock industry worldwide. These diseases considerably impact animal health and welfare and cause severe economic losses. One of the first lines of pathogen defense combines the respiratory tract mucus, a highly viscous material primarily composed of mucins, and a thriving multi-kingdom microbial ecosystem. The microbiome-mucin interplay protects from unwanted substances and organisms, but its dysfunction may enable pathogenic infections and the onset of respiratory disease. Emerging evidence also shows that noncoding regulatory RNAs might modulate the structure and function of the microbiome-mucin relationship. This opinion paper unearths the current understanding of the triangular relationship between mucins, the microbiome, and noncoding RNAs in the context of respiratory infections in animals of veterinary interest. There is a need to look at these molecular underpinnings that dictate distinct health and disease outcomes to implement effective prevention, surveillance, and timely intervention strategies tailored to the different epidemiological contexts.

Node features of chromosome structure networks and their connections to genome annotation

The 3D conformations of chromosomes can encode biological significance, and the implications of such structures have been increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from Hi-C pairwise genome contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. We investigate how additional properties of chromosome structure can be deduced using an abstract graph representation of the contact heatmap, and describe specific network properties that can have a strong connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties. These properties are useful for characterizing certain aspects of chromosomal structure. We examined the ability of network properties to differentiate several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, depletion of chromosome architectural proteins, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties exhibit greater capability in characterizing genome annotations compared to diffusive or ultra-local node features. For example, the local square clustering coefficient can be a strong classifier of lamina-associated domains. We demonstrated that network properties can be useful for highlighting large-scale chromosome structure differences that emerge in different biological situations.

Pathogenicity and virulence of Acinetobacter baumannii: Factors contributing to the fitness in healthcare settings and the infected host

Acinetobacter baumannii is a common cause of healthcare-associated infections and hospital outbreaks, particularly in intensive care units. Much of the success of A. baumannii relies on its genomic plasticity, which allows rapid adaptation to adversity and stress. The capacity to acquire novel antibiotic resistance determinants and the tolerance to stresses encountered in the hospital environment promote A. baumannii spread among patients and long-term contamination of the healthcare setting. This review explores virulence factors and physiological traits contributing to A. baumannii infection and adaptation to the hospital environment. Several cell-associated and secreted virulence factors involved in A. baumannii biofilm formation, cell adhesion, invasion, and persistence in the host, as well as resistance to xeric stress imposed by the healthcare settings, are illustrated to give reasons for the success of A. baumannii as a hospital pathogen.

The structure of inactivated mature tick-borne encephalitis virus at 3.0 Å resolution

Tick-borne encephalitis virus (TBEV) causes a severe disease, tick-borne encephalitis (TBE), that has a substantial epidemiological importance for Northern Eurasia. Between 10,000 and 15,000 TBE cases are registered annually despite the availability of effective formaldehyde-inactivated full-virion vaccines due to insufficient vaccination coverage, as well as sporadic cases of vaccine breakthrough. The development of improved vaccines would benefit from the atomic resolution structure of the antigen. Here we report the refined single-particle cryo-electron microscopy (cryo-EM) structure of the inactivated mature TBEV vaccine strain Sofjin-Chumakov (Far-Eastern subtype) at a resolution of 3.0 Å. The increase of the resolution with respect to the previously published structures of TBEV strains Hypr and Kuutsalo-14 (European subtype) was reached due to improvement of the virus sample quality achieved by the optimized preparation methods. All the surface epitopes of TBEV were structurally conserved in the inactivated virions. ELISA studies with monoclonal antibodies supported the hypothesis of TBEV protein shell cross-linking upon inactivation with formaldehyde.

Nucleic acid-binding KH domain proteins influence a spectrum of biological pathways including as part of membrane-localized complexes

K-Homology domain (KH domain) proteins bind single-stranded nucleic acids, influence protein-protein interactions of proteins that harbor them, and are found in all kingdoms of life. In concert with other functional protein domains KH domains contribute to a variety of critical biological activities, often within higher order machineries including membrane-localized protein complexes. Eukaryotic KH domain proteins are linked to developmental processes, morphogenesis, and growth regulation, and their aberrant expression is often associated with cancer. Prokaryotic KH domain proteins are involved in integral cellular activities including cell division and protein translocation. Eukaryotic and prokaryotic KH domains share structural features, but are differentiated based on their structural organizations. In this review, we explore the structure/function relationships of known examples of KH domain proteins, and highlight cases in which they function within or at membrane surfaces. We also summarize examples of KH domain proteins that influence bacterial virulence and pathogenesis. We conclude the article by discussing prospective research avenues that could be pursued to better investigate this largely understudied protein category.

Cysteine hyperoxidation rewires communication pathways in the nucleosome and destabilizes the dyad

Gene activity is tightly controlled by reversible chemical modifications called epigenetic marks, which are of various types and modulate gene accessibility without affecting the DNA sequence. Despite an increasing body of evidence demonstrating the role of oxidative-type modifications of histones in gene expression regulation, there remains a complete absence of structural data at the atomistic level to understand the molecular mechanisms behind their regulatory action. Owing to μs time-scale MD simulations and protein communication networks analysis, we describe the impact of histone H3 hyperoxidation (i.e., S-sulfonylation) on the nucleosome core particle dynamics. Our results reveal the atomic-scale details of the intrinsic structural networks within the canonical histone core and their perturbation by hyperoxidation of the histone H3 C110. We show that this modification involves local rearrangements of the communication networks and destabilizes the dyad, and that one modification is enough to induce a maximal structural signature. Our results suggest that cysteine hyperoxidation in the nucleosome core particle might favor its disassembly.

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

Dynamic duo: Kir6 and SUR in KATP channel structure and function

KATP channels are ligand-gated potassium channels that couple cellular energetics with membrane potential to regulate cell activity. Each channel is an eight subunit complex comprising four central pore-forming Kir6 inward rectifier potassium channel subunits surrounded by four regulatory subunits known as the sulfonylurea receptor, SUR, which confer homeostatic metabolic control of KATP gating. SUR is an ATP binding cassette (ABC) protein family homolog that lacks membrane transport activity but is essential for KATP expression and function. For more than four decades, understanding the structure-function relationship of Kir6 and SUR has remained a central objective of clinical significance. Here, we review progress in correlating the wealth of functional data in the literature with recent KATP cryoEM structures.

Beauveria bassiana transcriptomics reveal virulence-associated shifts during insect lipid assimilation

Insect cuticular lipids, especially epicuticular hydrocarbons (CHC), have a significant role in insect ecology and interactions with other organisms, including fungi. The CHC composition of a specific insect species may influence the outcome of the interaction with a specific fungal strain. Some insects, such as Piezodorus guildinii, have low susceptibility towards fungal infections seemingly due to their CHC composition. The entomopathogenic fungus Beauveria bassiana can assimilate CHC and incorporate them as building blocks via cytochrome P450 monooxygenases (CYPs). However, little is known about other enzymes that promote the degradation/assimilation of these cuticular components. In this study, we performed a transcriptomic analysis to evaluate the in vitro response of two virulence-contrasting B. bassiana strains when grown on three different P. guildinii CHC sources. We found a different expression profile of virulence-related genes, as well as different GO and KEGG parameters enriched at 4 days post-inoculation, which could help account for the intrinsic virulence and for an alkane-priming virulence enhancement effect. The hypovirulent strain predominantly showed higher expression of cuticle penetration genes, including chitinases, proteases, and CYPs, with GO term categories of "heme binding," "monooxygenase activity," and "peroxisome" pathways enriched. The hypervirulent strain showed higher expression of cell wall remodeling and cell cycle genes, and cuticle adhesion and a distinct set of CYPs, with GO categories of "DNA-binding transcription factor activity" and KEGG pathways corresponding to "meiosis-yeast" and "cell cycle" enriched. These results suggest a delay and alternate routes in pathogenicity-related metabolism in the hypovirulent strain in comparison with the hypervirulent strain. KEY POINTS: •Transcriptomics of two B. bassiana strains grown in P. guildinii cuticular components •Virulence-related genes correlated with virulence enhancement towards P. guildinii •Differentially expressed genes, GOs and KEGGs showed different metabolic timelines associated with virulence.

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.

Putative mechanism of a multivitamin treatment against insulin resistance

Insulin resistance is caused by the abnormal secretion of proinflammatory cytokines in adipose tissue, which is induced by an increase in lipid accumulation in adipocytes, hepatocytes, and myocytes. The inflammatory pathway involves multiple targets such as nuclear factor kappa B, inhibitor of nuclear factor κ-B kinase, and mitogen-activated protein kinase. Vitamins are micronutrients with anti-inflammatory activities that have unclear mechanisms. The present study aimed to describe the putative mechanisms of vitamins involved in the inflammatory pathway of insulin resistance. The strategy to achieve this goal was to integrate data mining and analysis, target prediction, and molecular docking simulation calculations to support our hypotheses. Our results suggest that the multitarget activity of vitamins A, B1, B2, B3, B5, B6, B7, B12, C, D3, and E inhibits nuclear factor kappa B and mitogen-activated protein kinase, in addition to vitamins A and B12 against inhibitor of nuclear factor κ-B kinase. The findings of this study highlight the pharmacological potential of using an anti-inflammatory and multitarget treatment based on vitamins and open new perspectives to evaluate the inhibitory activity of vitamins against nuclear factor kappa B, mitogen-activated protein kinase, and inhibitor of nuclear factor κ-B kinase in an insulin-resistant context.

Intra-strain colony biofilm heterogeneity in uropathogenic Escherichia coli and the effect of the NlpI lipoprotein

Biofilm growth facilitates the interaction of uropathogenic Escherichia coli (UPEC) with the host environment. The extracellular polymeric substances (EPS) of UPEC biofilms are composed prominently of curli amyloid fiber and cellulose polysaccharide. When the organism is propagated as a colony biofilm on agar media, these macromolecules can generate pronounced macroscopic structures. Moreover, curli/cellulose associate tightly with Congo red, generating a characteristic pink-to-red staining pattern when the media is supplemented with this dye. Among different clinical isolates of UPEC, changes in the abundance of curli/cellulose can lead to diverse colony biofilm phenotypes on a strain-by-strain basis. Nevertheless, for any given isolate, these phenotypes are classically homogenous throughout the colony biofilm. Here, we report that a subset of clinical UPEC isolates display heterogenous 'peppermint' colony biofilms, with distinct pale and red subpopulations. Through isolation of these subpopulations and whole genome sequencing, we demonstrate various emergent mutations associated with the phenomenon, including within the gene encoding the outer membrane lipoprotein nlpI. Deletion of nlpI within independent strain-backgrounds increased biofilm rugosity, while its overexpression induced the peppermint phenotype. Upregulation of EPS-associated proteins and transcripts was likewise observed in the absence of nlpI. Overall, these results demonstrate that EPS elaboration in UPEC is impacted by nlpI. More broadly, this phenomenon of intra-strain colony biofilm heterogeneity may be leveraged as a tool to identify additional members within the broad collection of genes that regulate or otherwise affect biofilm formation.

Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures

Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

MicroED structure of the C11 cysteine protease clostripain

Clostripain secreted from Clostridium histolyticum is the founding member of the C11 family of Clan CD cysteine peptidases, which is an important group of peptidases secreted by numerous bacteria. Clostripain is an arginine-specific endopeptidase. Because of its efficacy as a cysteine peptidase, it is widely used in laboratory settings. Despite its importance the structure of clostripain remains unsolved. Here we describe the first structure of an active form of C. histolyticum clostripain determined at 2.5 Å resolution using microcrystal electron diffraction (MicroED). The structure was determined from a single nanocrystal after focused ion beam milling. The structure of clostripain shows a typical Clan CD α/β/α sandwich architecture and the Cys231/His176 catalytic dyad in the active site. It has a large electronegative substrate binding pocket showing its ability to accommodate large and diverse substrates. A loop in the heavy chain formed between residues 452 and 457 is potentially important for substrate binding. In conclusion, this result demonstrates the importance of MicroED to determine the unknown structure of macromolecules such as clostripain, which can be further used as a platform to study substrate binding and design of potential inhibitors against this class of peptidases.

In silico and in vivo analysis reveal impact of c-Myc tag in FMC63 scFv-CD19 protein interface and CAR-T cell efficacy

Anti-CD19 CAR-T cell therapy represents a breakthrough in the treatment of B-cell malignancies, and it is expected that this therapy modality will soon cover a range of solid tumors as well. Therefore, a universal cheap and sensitive method to detect CAR expression is of foremost importance. One possibility is the use of epitope tags such as c-Myc, HA or FLAG tags attached to the CAR extracellular domain, however, it is important to determine whether these tags can influence binding of the CAR with its target molecule. Here, we conducted in-silico structural modelling of an FMC63-based anti-CD19 single-chain variable fragment (scFv) with and without a c-Myc peptide tag added to the N-terminus portion and performed molecular dynamics simulation of the scFv with the CD19 target. We show that the c-Myc tag presence in the N-terminus portion does not affect the scFv's structural equilibrium and grants more stability to the scFv. However, intermolecular interaction potential (IIP) analysis reveals that the tag can approximate the complementarity-determining regions (CDRs) present in the scFv and cause steric impediment, potentially disturbing interaction with the CD19 protein. We then tested this possibility with CAR-T cells generated from human donors in a Nalm-6 leukemia model, showing that CAR-T cells with the c-Myc tag have overall worse antitumor activity, which was also observed when the tag was added to the C-terminus position. Ultimately, our results suggest that tag addition is an important aspect of CAR design and can influence CAR-T cell function, therefore its use should be carefully considered.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Conformational plasticity of SpyCas9 induced by AcrIIA4 and AcrIIA2: Insights from molecular dynamics simulation

CRISPR-Cas9 systems constitute bacterial adaptive immune systems that protect against phage infections. Bacteriophages encode anti-CRISPR proteins (Acrs) that mitigate the bacterial immune response. However, the structural basis for their inhibitory actions from a molecular perspective remains elusive. In this study, through microsecond atomistic molecular dynamics simulations, we demonstrated the remarkable flexibility of Streptococcus pyogenes Cas9 (SpyCas9) and its conformational adaptability during interactions with AcrIIA4 and AcrIIA2. Specifically, we demonstrated that the binding of AcrIIA4 and AcrIIA2 to SpyCas9 induces a conformational rearrangement that causes spatial separation between the nuclease and cleavage sites, thus making the endonuclease inactive. This separation disrupts the transmission of signals between the protospacer adjacent motif recognition and nuclease domains, thereby impeding the efficient processing of double-stranded DNA. The simulation also reveals that AcrIIA4 and AcrIIA2 cause different structural variations of SpyCas9. Our research illuminates the precise mechanisms underlying the suppression of SpyCas9 by AcrIIA4 and AcrIIA2, thus presenting new possibilities for controlling genome editing with higher accuracy.

Sequence-based identification of amyloidogenic β-hairpins reveals a prostatic acid phosphatase fragment promoting semen amyloid formation

β-Structure-rich amyloid fibrils are hallmarks of several diseases, including Alzheimer's (AD), Parkinson's (PD), and type 2 diabetes (T2D). While amyloid fibrils typically consist of parallel β-sheets, the anti-parallel β-hairpin is a structural motif accessible to amyloidogenic proteins in their monomeric and oligomeric states. Here, to investigate implications of β-hairpins in amyloid formation, potential β-hairpin-forming amyloidogenic segments in the human proteome were predicted based on sequence similarity with β-hairpins previously observed in Aβ, α-synuclein, and islet amyloid polypeptide, amyloidogenic proteins associated with AD, PD, and T2D, respectively. These three β-hairpins, established upon binding to the engineered binding protein β-wrapin AS10, are characterized by proximity of two sequence segments rich in hydrophobic and aromatic amino acids, with high β-aggregation scores according to the TANGO algorithm. Using these criteria, 2505 potential β-hairpin-forming amyloidogenic segments in 2098 human proteins were identified. Characterization of a test set of eight protein segments showed that seven assembled into Thioflavin T-positive aggregates and four formed β-hairpins in complex with AS10 according to NMR. One of those is a segment of prostatic acid phosphatase (PAP) comprising amino acids 185-208. PAP is naturally cleaved into fragments, including PAP(248-286) which forms functional amyloid in semen. We find that PAP(185-208) strongly decreases the protein concentrations required for fibril formation of PAP(248-286) and of another semen amyloid peptide, SEM1(86-107), indicating that it promotes nucleation of semen amyloids. In conclusion, β-hairpin-forming amyloidogenic protein segments could be identified in the human proteome with potential roles in functional or disease-related amyloid formation.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Structural basis of the monkeypox virus mRNA cap N7 methyltransferase complex

The global outbreak of Mpox, caused by the monkeypox virus (MPXV), has attracted international attention and become another major infectious disease event after COVID-19. The mRNA cap N7 methyltransferase (RNMT) of MPXV methylates the N7 position of the added guanosine to the 5'-cap structure of mRNAs and plays a vital role in evading host antiviral immunity. MPXV RNMT is composed of the large subunit E1 and the small subunit E12. How E1 and E12 of MPXV assembly remains unclear. Here, we report the crystal structures of E12, the MTase domain of E1 with E12 (E1CTD-E12) complex, and the E1CTD-E12-SAM ternary complex, revealing the detailed conformations of critical residues and the structural changes upon E12 binding to E1. Functional studies suggest that E1CTD N-terminal extension (Asp545-Arg562) and the small subunit E12 play an essential role in the binding process of SAM. Structural comparison of the AlphaFold2-predicted E1, E1CTD-E12 complex, and the homologous D1-D12 complex of vaccinia virus (VACV) indicates an allosteric activating effect of E1 in MPXV. Our findings provide the structural basis for the MTase activity stimulation of the E1-E12 complex and suggest a potential interface for screening the anti-poxvirus inhibitors.

Boronic acid inhibitors of penicillin-binding protein 1b: serine and lysine labelling agents

Penicillin-binding proteins (PBPs) contribute to bacterial cell wall biosynthesis and are targets of antibacterial agents. Here, we investigated PBP1b inhibition by boronic acid derivatives. Chemical starting points were identified by structure-based virtual screening and aliphatic boronic acids were selected for further investigations. Structure-activity relationship studies focusing on the branching of the boron-connecting carbon and quantum mechanical/molecular mechanical simulations showed that reaction barrier free energies are compatible with fast reversible covalent binding and small or missing reaction free energies limit the inhibitory activity of the investigated boronic acid derivatives. Therefore, covalent labelling of the lysine residue of the catalytic dyad was also investigated. Compounds with a carbonyl warhead and an appropriately positioned boronic acid moiety were shown to inhibit and covalently label PBP1b. Reversible covalent labelling of the catalytic lysine by imine formation and the stabilisation of the imine by dative N-B bond is a new strategy for PBP1b inhibition.

Discovery of a novel marine Bacteroidetes with a rich repertoire of carbohydrate-active enzymes

Members of the phylum Bacteroidetes play a key role in the marine carbon cycle through their degradation of polysaccharides via carbohydrate-active enzymes (CAZymes) and polysaccharide utilization loci (PULs). The discovery of novel CAZymes and PULs is important for our understanding of the marine carbon cycle. In this study, we isolated and identified a potential new genus of the family Catalimonadaceae, in the phylum Bacteroidetes, from the southwest Indian Ocean. Strain TK19036, the type strain of the new genus, is predicted to encode CAZymes that are relatively abundant in marine Bacteroidetes genomes. Tunicatimonas pelagia NBRC 107804T, Porifericola rhodea NBRC 107748T and Catalinimonas niigatensis NBRC 109829T, which exhibit 16 S rRNA similarities exceeding 90% with strain TK19036, and belong to the same family, were selected as reference strains. These organisms possess a highly diverse repertoire of CAZymes and PULs, which may enable them to degrade a wide range of polysaccharides, especially pectin and alginate. In addition, some secretory CAZymes in strain TK19036 and its relatives were predicted to be transported by type IX secretion system (T9SS). Further, to the best of our knowledge, we propose the first reported "hybrid" PUL targeting alginates in T. pelagia NBRC 107804T. Our findings provide new insights into the polysaccharide degradation capacity of marine Bacteroidetes, and suggest that T9SS may play a more important role in this process than previously believed.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q 3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy

G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.

Accurate size-based protein localization from cryo-ET tomograms

Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an essential step in the cryo-ET/STA image analysis pipeline that consists in locating the position of proteins within crowded cellular tomograms so that they can be aligned and averaged in 3D to improve resolution. While extensive work in 2D particle picking has been done in the context of single-particle cryo-EM, comparatively fewer strategies have been proposed to pick particles from 3D tomograms, in part due to the challenges associated with working with noisy 3D volumes affected by the missing wedge. While strategies based on 3D template-matching and deep learning are commonly used, these methods are computationally expensive and require either an external template or manual labelling which can bias the results and limit their applicability. Here, we propose a size-based method to pick particles from tomograms that is fast, accurate, and does not require external templates or user provided labels. We compare the performance of our approach against a commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on non-specialized CPU hardware. We demonstrate the effectiveness of our approach by automatically detecting particles from tomograms representing different types of samples and using these particles to determine the high-resolution structures of ribosomes imaged in vitro and in situ.

A comprehensive investigation of the anion inhibition profile of a β-carbonic anhydrase from Acinetobacter baumannii for crafting innovative antimicrobial treatments

This study refers to the intricate world of Acinetobacter baumannii, a resilient pathogenic bacterium notorious for its propensity at antibiotic resistance in nosocomial infections. Expanding upon previous findings that emphasised the bifunctional enzyme PaaY, revealing unexpected γ-carbonic anhydrase (CA) activity, our research focuses on a different class of CA identified within the A. baumannii genome, the β-CA, designated as 𝛽-AbauCA (also indicated as CanB), which plays a crucial role in the resistance mechanism mediated by AmpC beta-lactamase. Here, we cloned, expressed, and purified the recombinant 𝛽-AbauCA, unveiling its distinctive kinetic properties and inhibition profile with inorganic anions (classical CA inhibitors). The exploration of 𝛽-AbauCA not only enhances our understanding of the CA repertoire of A. baumannii but also establishes a foundation for targeted therapeutic interventions against this resilient pathogen, promising advancements in combating its adaptability and antibiotic resistance.

Helix-to-sheet transition of the Aβ42 peptide revealed using an enhanced sampling strategy and Markov state model

The self-assembly of Aβ peptides into toxic oligomers and fibrils is the primary cause of Alzheimer's disease. Moreover, the conformational transition from helix to sheet is considered a crucial step in the aggregation of Aβ peptides. However, the structural details of this process still remain unclear due to the heterogeneity and transient nature of the Aβ peptides. In this study, we developed an enhanced sampling strategy that combines artificial neural networks (ANN) with metadynamics to explore the conformational space of the Aβ42 peptides. The strategy consists of two parts: applying ANN to optimize CVs and conducting metadynamics based on the resulting CVs to sample conformations. The results showed that this strategy achieved better sampling performance in terms of the distribution of sampled conformations. The sampling efficiency is increased by 10-fold compared to our previous Hamiltonian Exchange Molecular Dynamics (MD) and by 1000-fold compared to ordinary MD. Based on the sampled conformations, we constructed a Markov state model to understand the detailed transition process. The intermediate states in this process are identified, and the connecting paths are analyzed. The conformational transitions in D23-K28 and M35-V40 are proven to be crucial for aggregation. These results are helpful in clarifying the mechanism and process of Aβ42 peptide aggregation. D23-K28 and M35-V40 can be identified as potential targets for screening and designing inhibitors of Aβ peptide aggregation.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

A review of machine learning-based methods for predicting drug-target interactions

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

Cytoplasmic nucleoporin assemblage: the cellular artwork in physiology and disease

Nucleoporins, essential proteins building the nuclear pore, are pivotal for ensuring nucleocytoplasmic transport. While traditionally confined to the nuclear envelope, emerging evidence indicates their presence in various cytoplasmic structures, suggesting potential non-transport-related roles. This review consolidates findings on cytoplasmic nucleoporin assemblies across different states, including normal physiological conditions, stress, and pathology, exploring their structural organization, formation dynamics, and functional implications. We summarize the current knowledge and the latest concepts on the regulation of nucleoporin homeostasis, aiming to enhance our understanding of their unexpected roles in physiological and pathological processes.

New N-acylated aminoalkanoic acids from tea roots derived biocontrol agent Clonostachys rosea 15020

Four new N-acylated aminoalkanoic acids, namely clonoroseins E-H (1-4), together with three previously identified analogs, clonoroseins A, B, and D (5-7), were identified from the endophytic fungus Clonostachys rosea strain 15020 (CR15020), using Feature-based Molecular Networking (FBMN). The elucidation of their chemical structures, including their absolute configurations, was achieved through spectroscopic analysis combined with quantum chemical calculations. Bioinformatics analyses suggested that an iterative type I HR-PKS (CrsE) generates the polyketide side chain of these clonoroseins. Furthermore, a downstream adenylate-forming enzyme of the PKS (CrsD) was suspected to function as an amide synthetase. CrsD potentially facilitates the transformation of the polyketide moiety into an acyl-AMP intermediate, followed by nucleophilic substitution with either β-alanine or γ-aminobutyric acid to produce amide derivatives. These findings significantly expand our understanding of PKS-related products originating from C. rosea and also underscore the powerful application of FBMN analytical methods in characterization of new compounds.

Plant-based fascia tissues: Exploring materials and techniques for realistic simulation

The growing demand for sustainable and ethical food options has led to significant advancements in plant-based meat substitutes (PBMS). PBMS have made considerable progress in simulating the taste, texture, and sensory properties of animal meat. Connective tissue is a fundamental component of animal meat that significantly influences tenderness, texture, and sensory properties. However, the imitation of realistic connective tissues has received relatively less attention in the PBMS industry. The current work focuses on exploring materials and techniques for the replication of plant-based connective tissues (PBCT). By understanding the structural and functional characteristics of animal connective tissues (ACT), it is possible to replicate these characteristics in PBCT. Hydrogels, with their ability to simulate certain properties of ACT, present a viable material for the creation of PBCT. To achieve the desired simulation, their mechanical and structural properties need to be enhanced by using several materials and several physical techniques.