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The construction of coarse-grained descriptions of a system's kinetics is well established in biophysics. One prominent example is Markov state models in protein folding dynamics. In this paper, we develop a coarse-grained, discrete state model of a self-aggregating colloidal particle system inspired by the concepts of Markov state modeling. The specific self-aggregating system studied here involves field-responsive colloidal particles in orthogonal electric and magnetic fields. Starting from particle-resolved (Brownian dynamics) simulations, we define the discrete states by categorizing each particle according to its local structure. We then describe the kinetics between these states as a series of stochastic, memoryless jumps. In contrast to other works on colloidal self-assembly, our coarse-grained approach describes the simultaneous formation and evolution of multiple aggregates from single particles. Our discrete model also takes into account the changes in transition dynamics between the discrete states as the size of the largest cluster grows. We validate the coarse-grained model by comparing the predicted population fraction in each of the discrete states with those calculated directly from the particle-resolved simulations as a function of the largest cluster size. We then predict population fractions in the presence of noise-averaging and in a situation where a model parameter is changed instantaneously after a certain time. Finally, we explore the validity of the detailed balance condition in the various stages of aggregation. [ABSTRACT FROM AUTHOR]

Chemical phenomena involving near-degenerate electronic states, such as conical intersections or avoided crossing, can be properly described using quasi-degenerate perturbation theory. This study proposed a highly scalable quasi-degenerate second-order N-electron valence state perturbation theory (QD-NEVPT2) using the local pair-natural orbital (PNO) method. Our recent study showed an efficient implementation of the PNO-based state-specific NEVPT2 method using orthonormal localized virtual molecular orbitals (LVMOs) as an intermediate local basis. This study derived the state-coupling (or off-diagonal) terms to implement QD-NEVPT2 in an alternative manner to enhance efficiency based on the internally contracted basis and PNO overlap matrices between different references. To facilitate further acceleration, a local resolution-of-the-identity (RI) three-index integral generation algorithm was developed using LMOs and LVMOs. Although the NEVPT2 theory is considered to be less susceptible to the intruder-state problem (ISP), this study revealed that it can easily suffer from ISP when calculating high-lying excited states. We ameliorated this instability using the imaginary level shift technique. The PNO-QD-NEVPT2 calculations were performed on small organic molecules for the 30 lowest-lying states, as well as photoisomerization involving the conical intersection of 1,1-dimethyldibenzo[b,f] silepin with a cis-stilbene skeleton. These calculations revealed that the PNO-QD-NEVPT2 method yielded negligible errors compared to the canonical QD-NEVPT2 results. Furthermore, we tested its applicability to a large photoisomerization system using the green fluorescent protein model and the ten-state calculation of the large transition metal complex, showcasing that off-diagonal elements can be evaluated at a relatively low cost. [ABSTRACT FROM AUTHOR]

Phase separation of biomolecules can facilitate their spatiotemporally regulated self-assembly within living cells. Due to the selective yet dynamic exchange of biomolecules across condensate interfaces, condensates can function as reactive hubs by concentrating enzymatic components for faster kinetics. The principles governing this dynamic exchange between condensate phases, however, are poorly understood. In this work, we systematically investigate the influence of client–sticker interactions on the exchange dynamics of protein molecules across condensate interfaces. We show that increasing affinity between a model protein scaffold and its client molecules causes the exchange of protein chains between the dilute and dense phases to slow down and that beyond a threshold interaction strength, this slowdown in exchange becomes substantial. Investigating the impact of interaction symmetry, we found that chain exchange dynamics are also considerably slower when client molecules interact equally with different sticky residues in the protein. The slowdown of exchange is due to a sequestration effect, by which there are fewer unbound stickers available at the interface to which dilute phase chains may attach. These findings highlight the fundamental connection between client–scaffold interaction networks and condensate exchange dynamics. [ABSTRACT FROM AUTHOR]

A Markov state model is a powerful tool that can be used to track the evolution of populations of configurations in an atomistic representation of a protein. For a coarse-grained linear chain model with discontinuous interactions, the transition rates among states that appear in the Markov model when the monomer dynamics is diffusive can be determined by computing the relative entropy of states and their mean first passage times, quantities that are unchanged by the specification of the energies of the relevant states. In this paper, we verify the folding dynamics described by a diffusive linear chain model of the crambin protein in three distinct solvent systems, each differing in complexity: a hard-sphere solvent, a solvent undergoing multi-particle collision dynamics, and an implicit solvent model. The predicted transition rates among configurations agree quantitatively with those observed in explicit molecular dynamics simulations for all three solvent models. These results suggest that the local monomer–monomer interactions provide sufficient friction for the monomer dynamics to be diffusive on timescales relevant to changes in conformation. Factors such as structural ordering and dynamic hydrodynamic effects appear to have minimal influence on transition rates within the studied solvent densities. [ABSTRACT FROM AUTHOR]

Escherichia coli β-galactosidase was labelled with 1 mmol/L fluorescein 5-carbamoylmethylthiopropanoic N-hydroxysuccinimidyl ester for 1 and 3 min. The samples were separated by capillary electrophoresis and peak areas compared to that of labelled BSA for the purpose of quantification of the concentration of attached label. Enzyme concentration in the samples was determined by single molecule counting. The average number of labels attached to each molecule of enzyme was found to be 3.1 and 4.5 when labelled for 1 and 3 min, respectively. The distribution of single enzyme molecule electrophoretic mobilities for the unlabelled enzyme and that labelled for 1 and 3 min were measured using capillary electrophoresis. The average mobilities were found determined to be −(1.99 ± 0.13) × 10–8 m2 V−1 s−1 (N = 39), −(2.16 ± 0.19) × 10–8 m2 V−1 s−1 (N = 46), and –(2.18 ± 0.21) × 10–8 m2 V−1 s−1 (N = 39), respectively. A protein electrophoresis model was applied and predicted that the differences in average mobilities could be explained through relatively minor changes in overall charge, Stokes radius, and shape. This difference was similar to the range in mobilities observed in the unlabelled protein. This is consistent with the electrophoretic heterogeneity of the unmodified enzyme being caused by relatively small differences in charge, size, and shape of the individual molecules in the population. [ABSTRACT FROM AUTHOR]

• AB-Amy 2.0 is specifically designed to assess the amyloidogenic risk associated with therapeutic antibodies. • We utilize the pretrained protein language model to acquire light chain embeddings. • AB-Amy 2.0 offers a more robust tool for predicting the amyloidogenic risk of therapeutic antibody light chains. • The results clearly demonstrate that protein language model embeddings outperform the traditional features. Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww.ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies. [ABSTRACT FROM AUTHOR]

Aim: To expand the phenotypic spectrum of ADGRL1 and explore the correlation between epilepsy and the ADGRL1 genotype. Method: We performed whole‐exome sequencing in a cohort of 115 families (including 195 males and 150 females) with familial febrile seizure or epilepsy with unexplained aetiology. The damaging effects of variants was predicted using protein modelling and multiple in silico tools. All reported patients with ADGRL1 pathogenic variants were analysed. Results: One new ADGRL1 variant (p.Pro753Leu) was identified in one family with genetic epilepsy with febrile seizures. Further analysis of 12 ADGRL1 variants in 16 patients revealed that six patients had epilepsy. Epilepsy types ranged from early‐onset epileptic encephalopathy to genetic epilepsy with febrile seizures plus (GEFS+). All four variants associated with epilepsy were located in the non‐helix or sheet region of ADGRL1. Three of the four epilepsy‐associated variants were missense variants. Thus, all three variants located in the G‐protein‐coupled receptor autoproteolytic‐inducing domain exhibited epilepsy. Interpretation: We found one new missense variant of ADGRL1 in one family with GEFS+. ADGRL1 may be a potential candidate or susceptibility gene for epilepsy. ADGRL1‐associated epilepsy ranged from benign GEFS+ to severe epileptic encephalopathy; the genotypes and variant locations may help explain the phenotypic heterogeneity of patients with the ADGRL1 variant. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel approach is developed for manufacturing core annular hydrogels with a porous annulus. • The diffusivities of model proteins are measured in radial and axial directions. • The partition coefficients of model proteins are measured. • The porous hydrogels are imaged along different planes. • The SEM images and diffusivities and partition coefficients are used to assess pore connectivity and homogeneity of microstructure. Hypothesis : We propose to polymerize high water content hydroxyethyl methacrylate (HEMA) formulations in a rotating cylinder to explore the effect of the rotation on microstructure and critical parameters such as diffusivity of model proteins in porous poly-HEMA gels. Experiments : Cylindrical molds were partially filled with water-HEMA-initiator-crosslinker mixtures and exposed to UV light while undergoing rotation to polymerize into a cylindrical tube. The process was repeated multiple times to manufacture a core annular rod with multiple concentric rings, in which at least one ring was porous. The porous gels were imaged by scanning electron microscopy to explore the microstructure. The transport of model proteins bovine serum albumin and human γ-globulin was measured and modeled, in radial and axial directions, to obtain the effective diffusivity and partition coefficient. Also, the true diffusivity of proteins was calculated by accounting for the effects of porosity and tortuosity. Findings : The porous gels exhibited diffusion-controlled release of both model proteins. The hydrogels prepared with 55% water in the monomer mixture were porous with non-isotropic structure likely due to axially oriented pores with minimal radial connectivity. The gels with higher water content were isotropic with interconnected pores in both directions. The pore volume increased with water content, but the partition coefficient was relatively constant and less than one likely due to presence of isolated unconnected pores. [ABSTRACT FROM AUTHOR]

BACKGROUND: The citri red mite, Panonychus citri (McGregor), is an important citrus pest worldwide, causing enormous economic losses to citrus production. Bifenazate is a widely used acaricide for controlling P. citri. The detoxification mechanism of bifenazate is not clear in P. citri. RESULTS: PcGSTMu2, a significantly upregulated GST gene, was identified by the transcriptome analysis of P. citri after bifenazate exposure. The expression level of PcGSTMu2 was significantly increased after bifenazate exposure. By using RNAi of PcGSTMu2, the susceptibility of P. citri to bifenazate was significantly increased. Protein modeling and docking of PcGSTMu2 with GSH and bifenazate indicated the potential amino acid residues for binding in the active site. Heterologous expression and in vitro functional assays further revealed that PcGSTMu2 could deplete bifenazate. CONCLUSION: These results indicated that PcGSTMu2 plays an important role in the detoxification of bifenazate in P. citri and provides the molecular foundation for understanding bifenazate metabolism in P. citri. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: Multiple studies have shown that DNA methylation (DNAm) models of protein abundance can be informative about exposure, phenotype and disease risk. Here we investigate and provide descriptive details of the capacity of DNAm to capture non-genetic variation in protein abundance across the lifecourse. Methods: We evaluated the performance of 14 previously published DNAm models of protein abundance (episcores) in peripheral blood from a large adult population using the Avon Longitudinal Study of Parents and Children (ALSPAC) at ages 7–24 and their mothers antenatally and in middle age (N range = 145–1464). New age-specific episcores were trained in ALSPAC and evaluated at different ages. In all instances, episcore–protein associations were evaluated with and without adjustment for genetics. The association between longitudinal protein stability and longitudinal episcore projection was also evaluated, as was sex-specificity of episcores derived solely in female participants. Findings: Of the 14 Gadd episcores, 10 generated estimates associated with abundance in middle age, 9 at age 24, and none at age 9. Eight of these episcores explained variation beyond genotype in adulthood (6 at age 24; 7 at midlife). At age 9, the abundances of 22 proteins could be modelled by DNAm, 7 beyond genotype of which one trained model generated informative estimates at ages 24 and in middle age. At age 24, 31 proteins could be modelled by DNAm, 19 beyond genotype, of which 5 trained models generated informative estimates at age 9 and 8 in middle age. In middle age, 23 proteins could be modelled, 13 beyond genotype, of which 3 were informative at age 9 and 7 at age 24. Interpretation: We observed that episcores performed better at older ages than in children with several episcores capturing non-genetic variation at all ages. [ABSTRACT FROM AUTHOR]

Multimeric aptamer strategies are often adopted to improve the binding affinity of an aptamer toward its target molecules. In most cases, multimeric aptamers are constructed by connecting pre‐identified monomeric aptamers derived from in vitro selection. Although multimerization provides an added benefit of enhanced binding avidity, the characterization of different aptamer pairings adds more steps to an already lengthy procedure. Therefore, an aptamer engineering strategy that directly selects for multimeric aptamers is highly desirable. Here, an in vitro selection strategy is reported on using a pre‐structured DNA library that forms dimeric aptamers. Rather than using a library containing a single random region, which is nearly ubiquitous in existing aptamer selections, the library contains two random regions separated by a flexible poly‐thymidine linker. Following sixteen rounds of selection against the SARS‐CoV‐2 spike protein, a relevant model target protein due to the COVID‐19 pandemic, the top aptamers displayed superb affinity with KD values as low as 150 pM. Further analysis reveals that each random region functions as a distinct binding moiety and works together to achieve higher affinity. The demonstrated strategy provides an accelerated method to obtain high‐affinity aptamers, which may prove useful in future aptamer diagnostic and therapeutic applications. [ABSTRACT FROM AUTHOR]

A new round of monkeypox virus has emerged in the United Kingdom since July 2022 and rapidly swept the world. Currently, despite numerous research groups are studying this virus and seeking effective treatments, the information on the open reading frame, inhibitors, and potential targets of monkeypox has not been updated in time, and the comprehension of monkeypox target ligand interactions remains a key challenge. Here, we first summarized and improved the open reading frame information of monkeypox, constructed the monkeypox inhibitor library and potential targets library by database research as well as literature search, combined with advanced protein modeling technologies (Sequence-based and AI algorithms-based homology modeling). In addition, we build monkeypox virus Docking Server, a web server to predict the binding mode between targets and substrate. The open reading frame information, monkeypox inhibitor library, and monkeypox potential targets library are used as the initial files for server docking, providing free interactive tools for predicting ligand interactions of monkeypox targets, potential drug screening, and potential targets search. In addition, the update of the three databases can also effectively promote the study of monkeypox drug inhibition mechanism and provide theoretical guidance for the development of drugs for monkeypox. [ABSTRACT FROM AUTHOR]

Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development. Author summary: This study introduces IgBert and IgT5, two advanced antibody-specific language models capable of processing the extensive data generated by next-generation sequencing of antibodies. These models are trained on over two billion unpaired and two million paired antibody sequences from the Observed Antibody Space dataset. IgBert and IgT5 demonstrate state-of-the-art performance compared to existing models in tasks such as sequence recovery, expression, and binding affinity predictions. By employing comprehensive machine learning techniques and leveraging large-scale datasets, these models significantly enhance the potential for antibody design and therapeutic development. The trained models are publicly available, providing valuable tools for ongoing research in antibody engineering. [ABSTRACT FROM AUTHOR]

Globally, the prevalence of Rhipicephalus microplus resistance to various acaricides has increased, and there is a need for the identification of molecular markers that can predict phenotypic resistance. These markers could serve as alternatives to the larval packet test (LPT), enabling rapid and accurate monitoring of resistance in these ticks against multiple acaricides. However, many of the historically identified markers are present in isolates from specific countries and their role in acaricide resistance remains unclear. This study aimed to assess these mutations by sequencing genomic regions encoding proteins historically associated with acaricide target site insensitivity and increased acaricide detoxification and comparing resistant and susceptible isolates from eight different countries. Employing a novel multiplex PCR setup developed during the study, the coding regions of 11 acaricide-resistant targets were amplified and sequenced across 37 R. microplus isolates from different locations. The identified mutations, both previously reported and novel, were compared between acaricide-susceptible and acaricide-resistant isolates, phenotypically characterized using the larval packet test or larval immersion test across five acaricide classes. Genotypes were then correlated with available phenotypes, and protein modelling of novel nonsynonymous mutations was conducted to assess their potential impact on acaricide resistance. Previously reported resistance-associated mutations were detected, some of which were present in both resistant and susceptible isolates. Novel mutations emerged from the 11 targets, but distinctions between susceptible and resistant isolates were not evident, except for the prevalent kdr mutation in synthetic pyrethroid-resistant isolates. The quest for predictive molecular markers for monitoring acaricide resistance remains challenging. Nevertheless, by utilizing a representative group of isolates, we determined that several historical mutations were present in both resistant and susceptible isolates. Additionally, the study provides valuable genetic data on acaricide-resistant and susceptible isolates from different geographical regions, focusing on genomic regions implicated in resistance. This baseline data offers a critical foundation for further research and the identification of more reliable molecular markers. [ABSTRACT FROM AUTHOR]

Motivation Exploring human-virus protein–protein interactions (PPIs) is crucial for unraveling the underlying pathogenic mechanisms of viruses. Limitations in the coverage and scalability of high-throughput approaches have impeded the identification of certain key interactions. Current popular computational methods adopt a two-stream pipeline to identify PPIs, which can only achieve relation modeling of protein pairs at the classification phase. However, the fitting capacity of the classifier is insufficient to comprehensively mine the complex interaction patterns between protein pairs. Results In this study, we propose a pioneering single-stream framework HBFormer that combines hybrid attention mechanism and multimodal feature fusion strategy for identifying human-virus PPIs. The Transformer architecture based on hybrid attention can bridge the bidirectional information flows between human protein and viral protein, thus unifying joint feature learning and relation modeling of protein pairs. The experimental results demonstrate that HBFormer not only achieves superior performance on multiple human-virus PPI datasets but also outperforms 5 other state-of-the-art human-virus PPI identification methods. Moreover, ablation studies and scalability experiments further validate the effectiveness of our single-stream framework. Availability and implementation Codes and datasets are available at https://github.com/RmQ5v/HBFormer. [ABSTRACT FROM AUTHOR]

Motivation Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs. Results Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs. The framework exploits the primal and dual formulations of these models to derive sensitivity coefficients based on relations between variables, constraints, and the objective function. This approach enhances our understanding of the growth-limiting factors of metabolic phenotypes under specific environmental or genetic conditions. Compared to other traditional methods for calculating sensitivities, sEnz requires substantially less computation time and facilitates more intuitive comparison and analysis of sensitivities. The sensitivities calculated by sEnz cover enzymes, reactions and protein sectors, enabling a holistic overview of the factors influencing metabolism. When applied to an Escherichia coli PAM, sEnz revealed major pathways and enzymes driving overflow metabolism. Overall, sEnz offers a computational efficient framework for understanding PAM predictions and unraveling the factors governing a particular metabolic phenotype. Availability and implementation sEnz is implemented in the modular toolbox for the generation and analysis of PAMs in Python (PAModelpy; v.0.0.3.3), available on Pypi (https://pypi.org/project/PAModelpy/). The source code together with all other python scripts and notebooks are available on GitHub (https://github.com/iAMB-RWTH-Aachen/PAModelpy). [ABSTRACT FROM AUTHOR]

Motivation Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. These proteins exhibit distinct secondary structures that form repetitive tertiary arrangements, often resulting in large molecular assemblies. Despite highly variable sequences, STRPs can perform important and diverse biological functions, maintaining a consistent structure with a variable number of repeat units. With the advent of protein structure prediction methods, millions of 3D models of proteins are now publicly available. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation remains the most accurate method for detecting and classifying STRPs, making it impracticable to annotate millions of structures. Results We introduce STRPsearch, a novel tool for the rapid identification, classification, and mapping of STRPs. Leveraging manually curated entries from RepeatsDB as the known conformational space of STRPs, STRPsearch uses the latest advances in structural alignment for a fast and accurate detection of repeated structural motifs in proteins, followed by an innovative approach to map units and insertions through the generation of TM-score profiles. STRPsearch is highly scalable, efficiently processing large datasets, and can be applied to both experimental structures and predicted models. In addition, it demonstrates superior performance compared to existing tools, offering researchers a reliable and comprehensive solution for STRP analysis across diverse proteomes. Availability and implementation STRPsearch is coded in Python. All scripts and associated documentation are available from: https://github.com/BioComputingUP/STRPsearch. [ABSTRACT FROM AUTHOR]

Size is one of the important factors for antigen carriers because it influences cellular interaction, pharmacokinetics, immune cell activation and the types of immune responses. We developed pH-sensitive polysaccharide derivative-modified liposomes as antigen carriers for induction of antigen-specific immune responses. For an earlier study, the size of these liposomes was controlled to within 100–200 nm considering the suitable size for endocytosis. However, since most antigen-presenting cells can engulf larger particles, the proper liposome size and its effect on the induction of cellular versus humoral immune responses remains unclear. Here, we examined the effects of polysaccharide derivative-modified liposome size on immune responses. Liposome size was controlled by using an extrusion method by passing liposomes through a polycarbonate membrane of different pore sizes. Small liposomes encapsulating model antigenic protein suppressed antigen-expressing tumour growth effectively and increased the IgG2a/IgG1 ratio early after liposome administration, suggesting that Th1 response was mainly induced. Large liposomes increased antigen-specific IgG1 and IgG2a production for 56 days compared with small liposomes. These results suggest that small liposomes modified with pH-responsive polysaccharide derivatives could be effective for cancer immunotherapy whereas large liposomes could be suitable for induction of antibody production towards vaccination. [ABSTRACT FROM AUTHOR]

Pulmonary delivery and formulation of biologics are among the more complex and growing scientific topics in drug delivery. We herein developed a dry powder formulation using disordered mesoporous silica particles (MSP) as the sole excipient and lysozyme, the most abundant antimicrobial proteins in the airways, as model protein. The MSP had the optimal size for lung deposition (2.43 ± 0.13 µm). A maximum lysozyme loading capacity (0.35 mg/mg) was achieved in 150 mM PBS, which was seven times greater than that in water. After washing and freeze-drying, we obtained a dry powder consisting of spherical, non-aggregated particles, free from residual buffer, or unabsorbed lysozyme. The presence of lysozyme was confirmed by TGA and FT-IR, while N2 adsorption/desorption and SAXS analysis indicate that the protein is confined within the internal mesoporous structure. The dry powder exhibited excellent aerodynamic performance (fine particle fraction <5 µm of 70.32%). Lysozyme was released in simulated lung fluid in a sustained kinetics and maintaining high enzymatic activity (71–91%), whereas LYS-MSP were shown to degrade into aggregated nanoparticulate microstructures, reaching almost complete dissolution (93%) within 24 h. MSPs were nontoxic to in vitro lung epithelium. The study demonstrates disordered MSP as viable carriers to successfully deliver protein to the lungs, with high deposition and retained activity. [ABSTRACT FROM AUTHOR]

For decades researches have been conducted in Ethiopia to improve wheat productivity and quality. This involved extensive selection of breeding lines based on agronomic and laboratory data from numerous trials. However, generating laboratory data for all these services was both time-consuming and expensive. Hence, the study aimed to develop a very rapid and cost-effective testing method for wheat genotypes and varieties using near-infrared spectroscopy. For this purpose, we collected 155 bread wheat samples from high-growing potential areas of Sinana, Kulumsa, and Holeta. The wet chemistry analysis was done in duplicate following the international standard official methods. The same samples were also scanned using TANGO'S Bruker NIRS machine in duplicate to acquire spectral data. A calibration model was then developed by fitting the reference and spectral data using the partial least square (PLS). The calibration models exhibited a high level of strength for all parameters. The performance of the calibration model for protein (R2 = 0.99; RPD = 9.63), moisture (R2 = 0.96; RPD = 5.29), ash (R2 = 0.98; RPD = 6.39), total gluten (R2 = 0.98; RPD = 8.09), dry gluten (R2 = 0.97; RPD = 5.57), gluten index (R2 = 0.96; RPD = 5.23), and falling number (R2 = 0.97 and RPD = 6.23) were deemed suitable for wheat process and quality control purposes. But the models for moisture and gluten index (R2 = 0.96) were found to be suitable for screening and simple quality control purposes only. Therefore, this study clearly demonstrated that the near-infrared spectroscopy model is a highly reliable and robust method for predicting the key quality traits of bread wheat grains, serving the needs of both breeding programs and industry raw material control. [ABSTRACT FROM AUTHOR]

Salt-tolerant proteins, also known as halophilic proteins, have unique adaptations to function in high-salinity environments. These proteins have naturally evolved in extremophilic organisms, and more recently, are being increasingly applied as enzymes in industrial processes. Due to an abundance of salt-tolerant sequences and a simultaneous lack of experimental structures, most computational methods to predict stability are sequence-based only. These approaches, however, are hindered by a lack of structural understanding of these proteins. Here, we present HaloClass, an SVM classifier that leverages ESM-2 protein language model embeddings to accurately identify salt-tolerant proteins. On a newer and larger test dataset, HaloClass outperforms existing approaches when predicting the stability of never-before-seen proteins that are distal to its training set. Finally, on a mutation study that evaluated changes in salt tolerance based on single- and multiple-point mutants, HaloClass outperforms existing approaches, suggesting applications in the guided design of salt-tolerant enzymes. [ABSTRACT FROM AUTHOR]

The budding yeast Saccharomyces cerevisiae is commonly used as an expression platform for the production of valuable compounds. Yeast‐based genetic research can uniquely utilize auxotrophy in transformant selection: auxotrophic complementation by an auxotrophic marker gene on exogenous DNA (such as plasmids). However, the number of required auxotrophic nutrients restricts the number of plasmids maintained by the cells. We, therefore, developed novel Δ10 strains that are auxotrophic for 10 different nutrients and new plasmids with two multiple cloning sites and auxotrophic markers for use in Δ10 strains. We confirmed that Δ10 strains were able to maintain 10 types of plasmids. Using plasmids encoding model proteins, we detected the co‐expression of 17 different genes in Δ10 cell lines. We also constructed Δ9 strains that exhibited auxotrophy for nine nutrients and increased growth compared to Δ10. This study opens a new avenue for the co‐expression of a large number of genes in eukaryotic cells. [ABSTRACT FROM AUTHOR]

Engineering proteins is a challenging task requiring the exploration of a vast design space. Traditionally, this is achieved using Directed Evolution (DE), which is a laborious process. Generative deep learning, instead, can learn biological features of functional proteins from sequence and structural datasets and return novel variants. However, most models do not generate thermodynamically stable proteins, thus leading to many non-functional variants. Here we propose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which generates stable and functional variants by learning the sequence and thermodynamic landscape of a protein. We evaluate PREVENT by designing 40 variants of the conditionally essential E. coli phosphotransferase N-acetyl-L-glutamate kinase (EcNAGK). We find 85% of the variants to be functional, with 55% of them showing similar growth rate compared to the wildtype enzyme, despite harbouring up to 9 mutations. Our results support a new approach that can significantly accelerate protein engineering. Generative deep learning models can generate novel proteins, but these are often not functional due to thermodynamic instability. Here, authors present a method to learn both the sequence and thermodynamic landscape of a protein and generate new functional variants. [ABSTRACT FROM AUTHOR]

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants. [ABSTRACT FROM AUTHOR]

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering. [ABSTRACT FROM AUTHOR]

The de novo design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II helices, such as those found in collagen, remains relatively limited. In this study, we focus on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen Surfactant Protein A as a model. By dissecting the sequence derived from Surfactant Protein A and synthesizing short collagen-like peptides, we successfully construct a discrete bundle of hollow triple helices. Amino acid substitution studies pinpoint hydrophobic and charged residues that are critical for oligomer formation. These insights guide the de novo design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, two-dimensional nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix. Despite advances in machine learning approaches, de novo design of collagen-based materials remains difficult. In this study, based on the natural structure of the defense collagen family of proteins, designed triple helical peptide assemblies are found to form ribbons and a variety of bundled, porous architectures. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic‐resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT‐Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT‐Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty‐four peptides showed antimicrobial activity against both gram‐positive or gram‐negative bacteria. Among broad‐spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug‐resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user‐friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide‐based drug candidates in the future, as well as proteins with tailored characteristics. [ABSTRACT FROM AUTHOR]

Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 10.8%, 16.2%, and 4.9% in prokaryotic hosts, and 1.7%, 6.6% and 11.5% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic hosts and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL identifies important proteins in the prediction task. We found them capturing key functions in virus-host specificity. Author summary: Being able to predict which viruses can infect which host species, and identifying the specific proteins that are involved in these interactions, are fundamental tasks in virology. Traditional methods for predicting these interactions rely on identifying common features among proteins, overlooking the structure of the protein "language" encoded in individual proteins. We have developed a novel method that combines a protein language model and multiple instance learning to allow host prediction directly from protein sequences, without the need to extract features manually. This method significantly improved prediction accuracy and revealed key proteins involved in virus-host interactions. [ABSTRACT FROM AUTHOR]

The discovery of CRISPR-Cas systems has paved the way for advanced gene editing tools. However, traditional Cas discovery methods relying on sequence similarity may miss distant homologs and aren't suitable for functional recognition. With protein large language models (LLMs) evolving, there is potential for Cas system modeling without extensive training data. Here, we introduce CHOOSER (Cas HOmlog Observing and SElf-processing scReening), an AI framework for alignment-free discovery of CRISPR-Cas systems with self-processing pre-crRNA capability using protein foundation models. By using CHOOSER, we identify 11 Casλ homologs, nearly doubling the known catalog. Notably, one homolog, EphcCasλ, is experimentally validated for self-processing pre-crRNA, DNA cleavage, and trans-cleavage, showing promise for CRISPR-based pathogen detection. This study highlights an innovative approach for discovering CRISPR-Cas systems with specific functions, emphasizing their potential in gene editing. Identification of CRISPR-Cas systems by sequence alignment is limited by homolog sequence diversity. Here, the authors develop CHOOSER, and AI framework, for alignment free discovery of CRISPR-Cas systems, including EphcCasλ, which was validated for self-processing and DNA cleavage. [ABSTRACT FROM AUTHOR]

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets. Author summary: Accurate prediction of neoantigen immunogenicity has the potential to greatly improve the effectiveness of targeted therapies for cancer. While there are a number of associated tasks, such as peptide-HLA elution prediction, which can be now predicted with high accuracy by a number of published models, direct prediction of which epitopes will produce an immune response has proven more difficult. In this paper we demonstrate a modular protein language model approach which is trained iteratively to include data from related sub-tasks and can be extended to include information such as candidate TCRs of interest when available. There is a relatively small amount of immunogenicity data available, with even less data available with paired TCRs. This makes directly training a model to predict immunogenicity challenging. Our approach has the advantage of utilising data from sub-tasks and masked language modelling to allow for training a highly performant model with a small dataset. Using a cancer-specific benchmarking dataset we show this approach improves on existing state-of-the-art models and can be improved further with the addition of TCR context. This provides a framework that can serve as the basis for utilising additional information sources and datasets as they become available. [ABSTRACT FROM AUTHOR]

To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81–0.95) and external validation sets (AUC = 0.95; 95% CI 0.90–0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass. • Serum miRNA and protein biomarkers are complementary in estimating the risk of malignancy of an ovarian mass. • An integrated model with proteins, miRNA, and metadata offers the most accurate classification performance. • The addition of miRNA improves sensitivity and specificity for identification of early-stage ovarian cancer. [ABSTRACT FROM AUTHOR]

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains. Author summary: In immune receptors across various organisms, repeating protein structures play a crucial role in recognizing and responding to pathogen threats. These structures resemble the coils of a slinky toy, allowing these receptors to adapt and change over time. One particularly vital but challenging structure to study is the Leucine Rich Repeat (LRR). Traditional methods that rely just on analyzing the sequence of these proteins can miss subtle changes due to rapid evolution. With the introduction of protein structure prediction tools like AlphaFold 2, annotation methods can study the coarser geometric properties of the structure. In this study, we visualize LRR proteins in three dimensions and use a mathematical approach to 'flatten' them into two dimensions, so that the coils form circles. We then used a mathematical concept called winding number to determine the number of repeats and where they are in a protein sequence. This process helps reveal their repeating patterns with enhanced clarity. When we applied this method to immune receptors from a model plant organism, we found that our approach could accurately identify coiling patterns. Furthermore, we detected errors made by previous methods and highlighted unique structural variations. Our research offers a fresh perspective on understanding immune receptors, potentially influencing studies on their evolution and function. [ABSTRACT FROM AUTHOR]

Protein language models (pLMs) have emerged as potent tools for predicting and designing protein structure and function, and the degree to which these models fundamentally understand the inherent biophysics of protein structure stands as an open question. Motivated by a finding that pLM-based structure predictors erroneously predict nonphysical structures for protein isoforms, we investigated the nature of sequence context needed for contact predictions in the pLM Evolutionary Scale Modeling (ESM-2). We demonstrate by use of a "categorical Jacobian" calculation that ESM-2 stores statistics of coevolving residues, analogously to simpler modeling approaches like Markov Random Fields and Multivariate Gaussian models. We further investigated how ESM-2 "stores" information needed to predict contacts by comparing sequence masking strategies, and found that providing local windows of sequence information allowed ESM-2 to best recover predicted contacts. This suggests that pLMs predict contacts by storing motifs of pairwise contacts. Our investigation highlights the limitations of current pLMs and underscores the importance of understanding the underlying mechanisms of these models. [ABSTRACT FROM AUTHOR]

In living systems, protein assemblies have essential functions, serving as structural supports, transport highways for molecular cargo, and containers of genetic material. The construction of protein assemblies, which involves control over space and time, remains a significant challenge in biotechnology. Here, we show that anionic boron clusters, 3,3′‐commo‐bis[closo‐1,2‐dicarba‐3‐cobaltadodecaborane] (COSAN−), and halogenated closo‐dodecarboranes (B12X122−, X=H, Cl, or I), described as super‐chaotropic nano‐ions, induce the formation of 2D assemblies of model proteins, myoglobin, carbonic anhydrase, and trypsin inhibitor. We found that the nano‐ion concentration reversibly controls the size of the protein assemblies. Furthermore, the secondary structures of the proteins are only slightly affected by assembly formation. For myoglobin, the formation of these assemblies even prevents temperature denaturation, highlighting a preservation effect of nano‐ions. Our study reveals that inorganic boron‐based nano‐ions act as a reversible molecular glue for proteins, providing a potential starting point for the further development of controlled protein assemblies. [ABSTRACT FROM AUTHOR]

Introduction: Antibodies represent a specific class of proteins produced by the adaptive immune system in response to pathogens. Mining the information embedded in antibody amino acid sequences can benefit both antibody property prediction and novel therapeutic development. However, antibodies possess unique features that should be incorporated using specifically designed training methods, leaving room for improvement in pre-training models for antibody sequences. Methods: In this study, we present a Pre-trained model of Antibody sequences trained with a Rational Approach for antibodies (PARA). PARA employs a strategy conforming to antibody sequence patterns and an advanced natural language processing self-encoding model structure. This approach addresses the limitations of existing protein pre-training models, which primarily utilize language models without fully considering the differences between protein sequences and language sequences. Results: We demonstrate PARA's performance on several tasks by comparing it to various published pre-training models of antibodies. The results show that PARA significantly outperforms existing models on these tasks, suggesting that PARA has an advantage in capturing antibody sequence information. Discussion: The antibody latent representation provided by PARA can substantially facilitate studies in relevant areas. We believe that PARA's superior performance in capturing antibody sequence information offers significant potential for both antibody property prediction and the development of novel therapeutics. PARA is available at https://github.com/xtalpi-xic. [ABSTRACT FROM AUTHOR]

The response of soil carbon to climate change and anthropogenic forcing depends on the relationship between the physicochemical variables of the environment and microbial communities. In anoxic soils that store large amounts of organic carbon, it can be hypothesized that the low amount of catabolic energy available leads microbial organisms to minimize the energy costs of biosynthesis, which may shape the composition of microbial communities. To test this hypothesis, thermodynamic modeling was used to assess the link between redox gradients in the ombrotrophic peatland of the Marcell Experimental Forest (Minnesota, USA) and the chemical and taxonomic composition of microbial communities. The average amino acid composition of community‐level proteins, called hereafter model proteins, was calculated from shotgun metagenomic sequencing. The carbon oxidation state of model proteins decreases linearly from −0.14 at 10 cm depth to −0.17 at 150 cm depth. Calculating equilibrium activities of model proteins for a wide range of chemical conditions allows identification of the redox potential of maximum chemical activity. Consistent with redox measurements across peat soils, this model Eh decreases logarithmically from an average value of 300 mV at 10 cm depth, close to the stability domain of goethite relative to Fe2+, to an average value of −200 mV at 150 cm, within the stability domain of CH4 relative to CO2. The correlation identified between the taxonomic abundance and the carbon oxidation state of model proteins enables predicting the evolution of taxonomic abundance as a function of model Eh. The model taxonomic abundance is consistent with the measured gene and taxonomic abundance, which evolves from aerobic bacteria at the surface including Acidobacteria, Proteobacteria, and Verrumicrobia, to anaerobes at depth dominated by Crenarchaeota. These results indicate that the thermodynamic forcing imposed by redox gradient across peat soils shapes both the chemical and taxonomic composition of microbial communities. By providing a mechanistic understanding of the relationship between microbial community and environmental conditions, this work sheds new light on the mechanisms that govern soil microbial life and opens up prospects for predicting geochemical and microbial evolution in changing environments. [ABSTRACT FROM AUTHOR]

Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study. Thus, the use of transcriptomes obtained using the same pipeline as a proxy for the genetic content of species remains a valuable resource that is easier to obtain, cheaper, and more comparable than genomes. In a previous study, we presented the Metazoan Assemblies from Transcriptomic Ensembles database (MATEdb), a repository of high-quality transcriptomic and genomic data for the two most diverse animal phyla, Arthropoda and Mollusca. Here, we present the newest version of MATEdb (MATEdb2) that overcomes some of the previous limitations of our database: (i) we include data from all animal phyla where public data are available, and (ii) we provide gene annotations extracted from the original GFF genome files using the same pipeline. In total, we provide proteomes inferred from high-quality transcriptomic or genomic data for almost 1,000 animal species, including the longest isoforms, all isoforms, and functional annotation based on sequence homology and protein language models, as well as the embedding representations of the sequences. We believe this new version of MATEdb will accelerate research on animal phylogenomics while saving thousands of hours of computational work in a plea for open, greener, and collaborative science. [ABSTRACT FROM AUTHOR]

Summary We present ADTGP, an R package that uses Gaussian process regression to correct droplet-specific technical noise in single-cell protein sequencing data. ADTGP improves the interpretability of the data by modeling the distribution of protein expression, conditioned on equal isotype control counts across cells. ADTGP is written in R and needs only the protein raw counts, isotype control raw counts, and a design matrix to run. Availability and implementation ADTGP can be installed from https://github.com/northNomad/ADTGP. It depends on Stan and the R package 'cmdstanr'. [ABSTRACT FROM AUTHOR]

Summary A key challenge in antibody drug discovery is designing novel sequences that are free from developability issues—such as aggregation, polyspecificity, poor expression, or low solubility. Here, we present p-IgGen, a protein language model for paired heavy-light chain antibody generation. The model generates diverse, antibody-like sequences with pairing properties found in natural antibodies. We also create a finetuned version of p-IgGen that biases the model to generate antibodies with 3D biophysical properties that fall within distributions seen in clinical-stage therapeutic antibodies. Availability and implementation The model and inference code are freely available at www.github.com/oxpig/p-IgGen. Cleaned training data are deposited at doi.org/10.5281/zenodo.13880874. [ABSTRACT FROM AUTHOR]

Motivation O- linked glycosylation, an essential post-translational modification process in Homo sapiens , involves attaching sugar moieties to the oxygen atoms of serine and/or threonine residues. It influences various biological and cellular functions. While threonine or serine residues within protein sequences are potential sites for O- linked glycosylation, not all serine and/or threonine residues undergo this modification, underscoring the importance of characterizing its occurrence. This study presents a novel approach for predicting intracellular and extracellular O- linked glycosylation events on proteins, which are crucial for comprehending cellular processes. Two base multi-layer perceptron models were trained by leveraging a stacked generalization framework. These base models respectively use ProtT5 and Ankh O- linked glycosylation site-specific embeddings whose combined predictions are used to train the meta-multi-layer perceptron model. Trained on extensive O- linked glycosylation datasets, the stacked-generalization model demonstrated high predictive performance on independent test datasets. Furthermore, the study emphasizes the distinction between nucleocytoplasmic and extracellular O- linked glycosylation, offering insights into their functional implications that were overlooked in previous studies. By integrating the protein language model's embedding with stacked generalization techniques, this approach enhances predictive accuracy of O- linked glycosylation events and illuminates the intricate roles of O- linked glycosylation in proteomics, potentially accelerating the discovery of novel glycosylation sites. Results Stack-OglyPred-PLM produces Sensitivity, Specificity, Matthews Correlation Coefficient, and Accuracy of 90.50%, 89.60%, 0.464, and 89.70%, respectively on a benchmark NetOGlyc-4.0 independent test dataset. These results demonstrate that Stack-OglyPred-PLM is a robust computational tool to predict O- linked glycosylation sites in proteins. Availability and implementation The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/Stack-OglyPred-PLM. [ABSTRACT FROM AUTHOR]

Motivation Protein–protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination of single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction has still not been perfected. Additionally, experimentally determining structures is incredibly resource and time expensive. An alternative is the technique of computational docking, which takes the solved individual structures of proteins to produce candidate interfaces (decoys). Decoys are then scored using a mathematical function that assess the quality of the system, known as scoring functions. Beyond docking, scoring functions are a critical component of assessing structures produced by many protein generative models. Scoring models are also used as a final filtering in many generative deep learning models including those that generate antibody binders, and those which perform docking. Results In this work, we present improved scoring functions for protein–protein interactions which utilizes cutting-edge Euclidean graph neural network architectures, to assess protein–protein interfaces. These Eu clidean dock ing score models are known as EuDockScore, and EuDockScore-Ab with the latter being antibody–antigen dock specific. Finally, we provided EuDockScore-AFM a model trained on antibody–antigen outputs from AlphaFold-Multimer (AFM) which proves useful in reranking large numbers of AFM outputs. Availability and implementation The code for these models is available at https://gitlab.com/mcfeemat/eudockscore. [ABSTRACT FROM AUTHOR]

Motivation Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast missense amino acid variant effect predictor, leveraging protein language model (pLM) embeddings as input to a minimal deep learning model. Results To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. This setup increases interpretability compared to the baseline pLM and is easily retrainable with novel or updated pLMs. Assessed against the ProteinGym benchmark (217 multiplex assays of variant effect—MAVE—with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 ± 0.02, matching top-performing methods evaluated on the same data. VespaG has the advantage of being orders of magnitude faster, predicting all mutational landscapes of all proteins in proteomes such as Homo sapiens or Drosophila melanogaster in under 30 min on a consumer laptop (12-core CPU, 16 GB RAM). Availability and implementation VespaG is available freely at https://github.com/jschlensok/vespag. The associated training data and predictions are available at https://doi.org/10.5281/zenodo.11085958. [ABSTRACT FROM AUTHOR]

The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC. [ABSTRACT FROM AUTHOR]

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development. [ABSTRACT FROM AUTHOR]

Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein—a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited VS due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structures. [ABSTRACT FROM AUTHOR]

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines. [ABSTRACT FROM AUTHOR]

With SARS-CoV-2 N protein as a model target, a signal-enhanced LFIA based on Au@Pt nanoparticles (NPs) as labels is proposed. This Au@Pt NPs combined the distinguished localized surface plasma resonance (LSPR) effect of Au NPs and the ultrahigh peroxidase-like catalytic activity of Pt NPs. Au@Pt NPs could trigger substrate chromogenic reaction, generating a color signal orders of magnitude darker than their intrinsic color. In the detection, after the coloration of the strips, 3,3′,5,5′-tetramethylbenzidine (TMB) and H2O2 were added, and a dark blue chelate (OxTMB) was produced soon, enhancing the band color significantly. After the signal amplification, the naked-eye detection limit for N protein reached 40 pg/mL. The detection sensitivity enhanced more than 1000 times than that without signal amplification. Compared with mainstream LFIA requiring complex readout instruments, the Au@Pt-based LFIA achieved a comparable sensitivity using naked eyes detection. This point is crucial, especially for unprofessional users or low-resource areas. Hence, this signal-enhanced LFIA may serve as a sensitive, cost-effective, and user-friendly detection method. It can shorten the testing window period and help identify early infections. [ABSTRACT FROM AUTHOR]

Antifreeze proteins (AFPs) have unique features to sustain life in sub-zero environments due to ice recrystallization inhibition (IRI) and thermal hysteresis (TH). AFPs are in demand as agents in cryopreservation, but some antifreeze proteins have low levels of activity. This research aims to improve the cryopreservation activity of an AFPIV. In this in silico study, the helical peptide afp1m from an Antarctic yeast AFP was modeled into a sculpin AFPIV, to replace each of its four α-helices in turn, using various computational tools. Additionally, a new linker between the first two helices of AFPIV was designed, based on a flounder AFPI, to boost the ice interaction activity of the mutants. Bioinformatics tools such as ExPASy Prot-Param, Pep-Wheel, SOPMA, GOR IV, Swiss-Model, Phyre2, MODFOLD, MolPropity, and ProQ were used to validate and analyze the structural and functional properties of the model proteins. Furthermore, to evaluate the AFP/ice interaction, molecular dynamics (MD) simulations were executed for 20, 100, and 500 ns at various temperatures using GROMACS software. The primary, secondary, and 3D modeling analysis showed the best model for a redesigned antifreeze protein (AFP1mb, with afp1m in place of the fourth AFPIV helix) with a QMEAN (Swiss-Model) Z score value of 0.36, a confidence of 99.5%, a coverage score of 22%, and a p value of 0.01. The results of the MD simulations illustrated that AFP1mb had more rigidity and better ice interactions as a potential cryoprotectant than the other models; it also displayed enhanced activity in limiting ice growth at different temperatures. [ABSTRACT FROM AUTHOR]

Isothermal amplification (IA) techniques like rolling circle amplification (RCA) and loop-mediated isothermal amplification (LAMP) have gained significant attention in recent years due to their ability to rapidly amplify DNA or RNA targets at a constant temperature without the need for complex thermal cycling equipment. Such technologies, combined with colorimetric systems rendering visual confirmation of the amplification event, are ideal for the development of point-of-need detection methods suitable for field settings where access to specialized laboratory equipment is limited. The utility of these technologies, thus far limited to DNA and RNA targets, could be broadened to a wide range of targets by using aptamers. Composed of DNA or RNA themselves, aptamers can bind to substances, including proteins, metabolites, and inorganic substances. Their nucleic acid nature can potentially allow them to serve as a bridge, extending the reach of DNA/RNA-centric technologies to the broader molecular world. Indeed, the change in aptamer conformation occurring during ligand interaction can be used to elaborate ligand-responding RCA or LAMP templates. By using an existing aptamer targeting SARS-CoV-2 Spike protein as a model, we explored the possibility of establishing ligand-responsive IA systems. Our study used aptamers with simple sequence modifications as templates in LAMP assays and hyperbranched RCA (HRCA) by exploiting the dynamic nature of the model aptamer to trigger these IA systems. Importantly, our work uniquely demonstrates that this aptamer's dynamic response to ligand binding can regulate both RCA and LAMP processes. This novel approach of using aptamer conformational changes to trigger LAMP paves the way for new aptamer-based detection assays. Our system detects 50 nM of Spike protein, with LAMP occurring within 30 min in the presence of Spike. The colorimetric readout showed clear results, allowing for the detection of Spike protein presence. [ABSTRACT FROM AUTHOR]

Polysulfone (Psf) ultrafiltration flat-sheet membranes were modified with hydroxyapatite (HA) powder during preparation using the wet-phase inversion method. HA was incorporated to enhance the protein separation capabilities. The asymmetric Psf membranes were synthesized using NMP as the solvent. Through Scanning Electron Microscopy (SEM) analysis, it was revealed that HA was distributed across the membrane. Incorporating HA led to higher flux, the improved rejection of protein, and enhanced surface hydrophilicity. The permeability flux increased with HA concentration, peaking at 0.3 wt.%, resulting in a 38% improvement to 65 LMH/bar. Whey protein separation was evaluated using the model proteins BSA and lysozyme, representing α-Lactalbumin. The results of protein rejection for the blend membranes indicated that the rejection rates for BSA and lysozyme increased to 97.2% and 73%, respectively. Both the native and blend membranes showed similar BSA rejection rates; however, the blend membranes demonstrated better performance in lysozyme separation, indicating superior selectivity compared to native membranes. The modified membranes exhibited improved hydrophilicity, with water contact angles decreasing from 66° to 53°, alongside improved antifouling properties, indicated by a lower flux decline ratio value. This simple and economical modification method enhances permeability without sacrificing separation efficiency, hence facilitating the scalability of membrane production in the whey protein separation industry. [ABSTRACT FROM AUTHOR]