SoluProt: Prediction of Soluble Protein Expression in Escherichia coli

Sequence and taxonomic feature evaluation facilitated the discovery of alcohol oxidases

Recent advancements in data technology offer immense opportunities for the discovery and development of new enzymes for the green synthesis of chemicals. Current protein databases predominantly prioritize overall sequence matches. The multi-scale features underpinning catalytic mechanisms and processes, which are scattered across various data sources, have not been sufficiently integrated to be effectively utilized in enzyme mining. In this study, we developed a sequence- and taxonomic-feature evaluation driven workflow to discover enzymes that can be expressed in E. coli and catalyze chemical reactions in vitro, using alcohol oxidase (AOX) for demonstration, which catalyzes the conversion of methanol to formaldehyde. A dataset of 21 reported AOXs was used to construct sequence scoring rules based on features, including sequence length, structural motifs, catalytic-related residues, binding residues, and overall structure. These scoring rules were applied to filter the results from HMM-based searches, yielding 357 candidate sequences of eukaryotic origin, which were categorized into six classes at 85 % sequence similarity. Experimental validation was conducted in two rounds on 31 selected sequences representing all classes. Among these selected sequences, 19 were expressed as soluble proteins in E. coli, and 18 of these soluble proteins exhibited AOX activity, as predicted. Notably, the most active recombinant AOX exhibited an activity of 8.65 ± 0.29 U/mg, approaching the highest activity of native eukaryotic enzymes. Compared to the established UniProt-annotation-based workflow, this feature-evaluation-based approach yielded a higher probability of highly active recombinant AOX (from 8.3 % to 19.4 %), demonstrating the efficiency and potential of this multi-dimensional feature evaluation method in accelerating the discovery of active enzymes.

Expression of recombinant swine ferritin heavy chain with enhanced solubility in Escherichia coli and simplified purification of ferritin nanoparticles

Ferritin is a versatile biomolecule used in various medical applications such as drug delivery, vaccines, biological imaging, and diagnostics. The purity and concentration of the ferritin nanoparticles are crucial for achieving excellent outcomes. In this study, we expressed and purified the recombinant swine ferritin heavy chain (rsFTH) as a new candidate for recombinant ferritin nanoparticles. We generated two types of plasmids that can express rsFTH in mammalian and prokaryotic systems. The myc-tagged rsFTH expressed in the mammalian system was purified and ferritin nanoparticles were validated using dynamic light scattering (DLS) and transmission electron microscopy (TEM). A prokaryotic expression system was used to produce rsFTH on a large scale. Protein expression was optimized in Escherichia coli BL21 under varying temperatures and IPTG conditions, and solubility was enhanced by incubation at 25 °C for 18-22 h in auto-induction media, resulting in approximately >50 % protein content in the soluble fraction compared with the pellet. Protein purification was achieved using His-tag affinity chromatography and dialysis with Tris-HCl buffer, yielding adequately pure rsFTH without any apparent protein aggregates. SDS-PAGE and Western blot analysis confirmed the expected molecular weight of rsFTH, and Native-PAGE demonstrated polymerization into higher molecular weight forms. Particle size analysis of purified rsFTH revealed a mean diameter of 15.5 nm, with transmission electron microscopy (TEM) imaging confirming spherical ferritin particles with an iron core. These results suggest that rsFTH can be efficiently expressed and purified in both mammalian and bacterial systems, and has potential applications in nanotechnology and biotechnology.

Structural insights and predictive modelling of a novel anti-HER2 scFv and Leptulipin: a newly designed immunotoxin protein for HER2 positive cancers

Targeted therapy is one crucial therapeutic approach frequently employed in cancer treatment. In almost 30% of human breast cancers, a transmembrane tyrosine kinase receptor named the HER2 (human epidermal growth factor receptor 2) is overexpressed, establishing HER-2 as a promising target for cancer treatment. The goal of the current work is to computationally design and analyze a new chimeric protein that could selectively target HER2-positive breast cancer cells based on a single polypeptide chain variable fragment and leptulipin (an anticancer peptide) fusion. After the computational joining of the secondary structure, 3D modeling, quality validation, physicochemical properties, docking, interaction analysis, MD simulation, and energy calculations were performed using various computational tools and online servers. The most precise predicted chimeric protein model was docked to the HER-2 receptor using ClusPro 2.0, which revealed a significant number of hydrogen bonds and salt bridges reflecting the fusion protein's quality, validity, interaction, and stability. These results were further supported by MD simulation on the Desmond Schrodinger module, which predicted a stable docked complex. This was also evident by principal component analysis and the negative energy value of MM/PBSA. These comprehensive in silico analyses, coupled with a high predicted expression value of 0.94 in E. coli by the SOLUPROT, collectively highlight the potential of fusion protein as a potent therapeutic agent against breast cancer and open a potential avenue for targeted cancer therapy and provide a groundwork for in vitro and in vivo validation that might lead to clinical implication.

MTPSol: Multimodal Twin Protein Solubility Prediction Architecture Based on Pretrained Models

In the process of mining and de novo designing of new enzymes, the solubility of proteins is one of the key factors determining the efficiency of their functional expression. The development of solubility prediction algorithms is important for reducing experimental costs and enhancing the success of protein engineering. However, only a small number of studies have involved the input of protein structural information, which extremely limited the models' accuracy and generalization. Here, we developed a protein solubility prediction architecture named MTPSol by utilizing pretrained models to extract protein features and process the multimodal input of proteins. To further improve the performance of the architecture, cross-modal twin attention and multiscale feature networks were developed to integrate the multimodal features. Evaluating MTPSol with public benchmark data sets, MTPSol demonstrates that the architecture achieves competitive predictive performance. In the assessment conducted on our constructed and validated transaminase data set, MTPSol outperformed existing state-of-the-art models, further attests the architecture's generalization across different protein families. We firmly believe that MTPSol not only offers a more efficient screening method for the discovery of natural enzymes but also holds significant potential in the field of protein de novo design.

Comprehensive immunoinformatics and bioinformatics strategies for designing a multi-epitope based vaccine targeting structural proteins of Nipah virus

Background

Nipah virus (NiV) is characterized by recurring outbreaks and causes severe neurological impact, leading to increased mortality rates. Despite the severity of the disease, there is no proven post-exposure treatment available, emphasizing the critical need for the development of an effective vaccine.

Objective

This study was aimed at designing a multi-epitope based vaccine candidate based on an in-silico approach.

Methods

NiV's Structural proteins were screened for B and T-cell epitopes, assessing characteristics like antigenicity, immunogenicity, allergenicity, and toxicity. Two vaccine constructs (NiV_1 & 2) were designed using different adjuvants (Cholera toxin and Beta-defensin 3) and linkers and their predicted 3D structures were evaluated for interaction with Toll-Like Receptor TLR-3 using docking and molecular dynamics (MD) simulation studies. Finally, The potential expression of the vaccine construct in Escherichia coli (E. coli.) was verified by cloning it into the PET28a (+) vector and immune simulations were undertaken.

Results

The study identified 30 conserved, antigenic, immunogenic, non-allergenic, and non-toxic epitopes with a broad population coverage. Based on the stability of vaccine construct in MD simulations results, NiV_1 was considered for further analysis. In-silico immune simulations of NiV_1 indicated a substantial immunogenic response. Moreover, codon optimization and in-silico cloning validated the expressions of designed vaccine construct NiV_1 in E. coli.

Conclusion

The findings indicate that the NiV_1 vaccine construct has the potential to elicit both cellular and humoral immune responses. Additional in vitro and in vivo investigations are required to validate the computational observations.

Immunoinformatic-driven design and evaluation of multi-epitope mRNA vaccine targeting HIV-1 gp120

HIV (human immunodeficiency virus) presents a global health crisis, causing significant AIDS-related deaths and over one million new infections annually. The curbing of HIV is an intricate and continuously evolving domain, marked by numerous challenges, including drug resistance and the absence of a significant cure or vaccine because of its mutating ability and diverse antigens in its envelope, prompting research for functional cures and long-term remission strategies. The endeavor to devise an HIV vaccine capable of eliciting robust and broadly cross-reactive humoral and cellular immune responses is a formidable undertaking, primarily due to the pronounced genetic heterogeneity of HIV-1, the variances observed in virus subtypes (clades) across distinct geographic regions, and the polymorphic nature of human leukocyte antigens (HLA). The viral envelope protein (gp120) selectively interacts with CD4 and chemokine receptors on the surface of target cells. It serves as the key initiator in the intricate viral entry into host cells, rendering it a compelling candidate for vaccine development. This study used bioinformatic tools to design a safe, hypoallergenic, and non-toxic mRNA HIV-1 vaccine by assembling immunogenic B- and T-cell epitopes from the gp120 protein. We identified antigenic, non-toxic, and non-allergic B-cell epitopes (IEPLGIAPTRAKRRVVER) and T-cell epitopes (QQKVHALFY, ITIGPGQVF, WQGVGQAMY, APTRAKRRV, KQQKVHALFYRLDIV, QQKVHALFYRLDIVQ, QKVHALFYRLDIVQI, SLAEEEIIIRSENLT, and IRSENLTNNVKTIIV). For designing the mRNA vaccine against HIV-1 gp120, we assembled the epitopes with 5' m7G cap, 5' UTR (untranslated region), Kozak sequence, signal peptide (tPA), RpfE (resuscitation-promoting factor E) adjuvant at N-terminal and MITD (MHC class I trafficking domain) adjuvant, stop codon, 3' UTR, and 120-nucleotide long poly(A) tail at the C-terminal with immunogenic robustness linkers. The mRNA vaccine is translated into a protein-based vaccine by the host body's ribosomes. Their comprehensive computational findings, including physicochemical, structural, and 3D refinement analyses, substantiated the stability and quality of the translated vaccine. Molecular docking and simulation revealed a strong and stable binding affinity of vaccine immunization with immune cells' pattern recognition receptors (TLR4). Immune simulations demonstrated a potent primary immune response characterized by a gradual increase in immunoglobulins and a corresponding decline in antigen concentration. This bioinformatics-driven study presents a promising HIV-1 mRNA vaccine candidate, underscoring the need for further experimental validation through preclinical and clinical trials. At the same time, its methodologies hold the potential for addressing other challenging infectious diseases, thereby impacting vaccinology broadly.

COLOR: A Compositional Linear Operation-Based Representation of Protein Sequences for Identification of Monomer Contributions to Properties

The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is challenging due to the complexity of sequential data. While deep learning (DL) models can accurately capture sequence-property relationships, the degree of nonlinearity in these models limits the assessment of monomer contributions to a property─a critical step in identifying key motifs. Recent advances in explainable AI (XAI) offer attention and gradient-based methods for estimating monomeric contributions. However, these methods are primarily applied to classification tasks, such as binding site identification, where they achieve limited accuracy (40-45%) and rely on qualitative evaluations. To address these limitations, we introduce a DL model with interpretable steps, enabling direct tracing of monomeric contributions. Inspired by the masking technique commonly used in vision and natural language processing domains, we propose a new metric ( I ) for quantitative analysis on datasets mainly containing distinct properties of anticancer peptides (ACP), antimicrobial peptides (AMP), and collagen. Our model exhibits 22% higher explainability than the gradient and attention-based state-of-the-art models, recognizes critical motifs (RRR, RRI, and RSS) that significantly destabilize ACPs, and identifies motifs in AMPs that are 50% more effective in converting non-AMPs to AMPs. These findings highlight the potential of our model in guiding mutation strategies for designing protein-based biomaterials.

Investigating the structural and functional snapshots of Bacillus licheniformis alpha-amylase through protein engineering strategies

Alpha-amylases are essential enzymes that cleave α-1,4-glycosidic bonds in starch, generating products such as glucose, maltose, dextrin's, and oligosaccharides, which play a key role in various industries. The structural and functional insights, along with biochemical dynamics, of Bacillus licheniformis α-amylase variants (BLAMWSP and BLAMCD) were investigated through N- and C-terminal truncations along with two previously reported mutations, Thr353Ile and His400Arg. MD simulation results demonstrated the stability of binding and catalytic residues as predicted through molecular docking. Analysis of the secondary structure and temperature ramping through CD spectroscopy revealed that both BLAMWSP and BLAMCD maintained structural stability at 90 °C. The specific activities of BLAMWSP and BLAMCD against wheat starch were determined to be 2343.09 ± 0.20 and 4237.88 ± 0.66 (μmol min-1/μmole protein), respectively at 90 °C in 100 mM phosphate buffer (pH 6.0). The BLAMCD variant exhibited a two-fold increase in enzymatic activity relative to BLAMWSP due to increased surface accessibility of its substrate-binding and catalytic residues. The enzymes' catalytic efficiencies (kcat /Km) were 51.76 ± 1.76 and 114.10 ± 1.41, highlighting that BLAMCD exhibits significantly higher catalytic efficiency and substrate affinity than BLAMWSP. These modifications make BLAMCD a promising candidate for industrial starch liquefaction and scarification applications.

Immunoinformatics based designing of a multi-epitope cancer vaccine targeting programmed cell death ligand 1

Tumor cells express programmed cell death ligand 1 (PD-L1), which recognizes the immune checkpoint molecule programmed cell death 1 (PD-1) on T cells, suppressing the antitumor immune response. Inhibiting the PD-1:PD-L1 interaction has the potential to reactivate the immune response against tumors. Recent advancements in cancer therapy have demonstrated remarkable promise of immunotherapy, which exploits immune checkpoint inhibition by small molecules or monoclonal antibodies. This strategy has shown impressive clinical success in treating a wide range of cancer subtypes, albeit with certain limitations. This study aims to design a novel multi-epitope vaccine against PD-L1 by using an immunoinformatics approach. For attaining enhanced efficacy and minimize side effects, the vaccine was constructed using antigenic, non-allergenic, and non-toxic epitopes (5 CTL, 3 HTL, and 2 B-cell epitopes) predicted from the IgV domain of PD-L1. The vaccine design includes a large ribosomal subunit protein bL12 adjuvant, a 6xHis tag for purification, and appropriate linkers to connect the epitopes. The modelled 3D structure of the vaccine construct was docked with TLR4 immune receptor, demonstrating strong antigenic properties and stable binding, as validated by molecular dynamics simulations. Immune simulation studies suggest that the vaccine construct could potentially elicit significant immune regulators such as B cells, T-cells, and memory cells. Thus, the findings indicate that the vaccine may effectively suppress the PD-1:PD-L1 axis by targeting PD-L1, restoring the anticancer immune response. However, its efficacy needs to be validated in both in vitro and in vivo settings.

Multivalent vaccine candidate from conserved immunogenic peptides in entry or exit proteins of Orthopoxvirus genus

Orthopoxvirus (OPXV) genus includes emerging and re-emerging zoonotic viruses that pose threats to global health. Smallpox caused pandemics in the 20th century. Borealpox was responsible for a death in Alaska in 2024. Mpox, declared a Public Health Emergency by the WHO in 2022, with an alert reclassification in 2024. The lack of effective therapies and the limitations of attenuated virus vaccines, especially for immunocompromised individuals, reinforce the urgent need for new strategies to prevent diseases caused by pathogens of the OPXV genus. This study aimed to identify conserved epitopes in proteins essential for the entry and exit of these viruses and, based on this identification, develop a promising multivalent vaccine candidate. Viral protein sequences were extracted from the NCBI Virus database, and 160 sequences were analyzed to identify conserved epitopes using the Immune Epitope Database. After filtering the data, epitopes were concatenated to create a chimeric multi-epitope protein combined with β-defensin and PADRE adjuvants. The resulting protein, with eight conserved epitopes covering all OPXV viruses (including Mpox Clade Ib), was evaluated for antigenicity, allergenicity, and structural stability. It showed strong interaction with the TLR2 receptor, along with good predictions for immune responses after three doses. This proposed multivalent vaccine represents a potential approach against these zoonotic viruses, with promising results for in vitro and in vivo studies.

Protocol for designing a peptide-based multi-epitope vaccine targeting monkeypox using reverse vaccine technology

Reverse vaccine technology, supported by advancements in immunoinformatics, facilitates the development of multi-epitope vaccines for rapidly evolving pathogens, thereby strengthening the immune defense. Here, we present a protocol for a peptide-based multi-epitope vaccine targeting monkeypox virus (MPXV) using an open-source approach. We describe steps for evaluating physicochemical properties and allergenicity. We then detail procedures for validating pattern recognition receptor (PRR)-binding affinity and stable major histocompatibility complex (MHC) I/II presentation. Molecular dynamics (MD) simulations confirm immune receptor interactions, enhancing vaccine stability. For complete details on the use and execution of this protocol, please refer to Kaur et al.1.

In silico analysis and gene expression patterns of lignin peroxidase isozymes in Phanerochaete chrysosporium

Phanerochaete chrysosporium (Pc), is a prominent lignin-degrading fungus which serves as an important source for lignin-degrading enzymes (LDEs). The present study was focused on a detailed in silico analysis and gene expression patterns of lignin peroxidases (PcLiPs), which is a significant class of LDEs. In spite of extensive research on P. chrysosporium enzymes, the number of PcLiP isozymes remains unexplored. In the present study, ten PcLiP sequences were identified by the RedoXiBase and BLAST survey, displaying putative glycosylated extracellular protein which was approximately 38 to 39 kDa. Different domains of the protein included putative binding sites for stress, nutrient components, metal ions, peroxidase motifs, ligninase motifs, and also secretory signal peptides. Molecular docking analysis of all the PcLiPs, showed that the PcLiP4 had strong binding affinity towards hydrogen peroxide (H2O2), manganese (II) sulfate (MnSO4), and veratryl alcohol (VA) as compared to other PcLiPs. In order to analyze the PcLiPs gene expression, the fungus was incubated in potato dextrose broth medium (PDB). Notably, high expression levels of PcLiPs were observed during the 48-h growth stage of the fungus and there was variable gene expression under conditions of incubation with different stress factors and co-factors, such as H2O2, MnSO4, VA, and heat stress. Among the ten PcLiPs characterized, isozymes, such as, PcLiP4, PcLiP9, PcLiP10, and PcLiP8 exhibited varying concentrations of nutritional elements and stress levels together with high expression. Present study employing in silico analysis, molecular docking studies, and gene expression analysis demonstrated that the PcLiP4 could be an ideal candidate for lignin biodegradation. Results showed the operation of specific regulatory mechanisms which govern PcLiPs expression. As an outcome, regulatory factors towards obtaining high yield of PcLiPs and the best isozyme for heterologous gene expression were identified. These findings would contribute to enhancing the efficiency of biodegradation of lignocelluloses and related recalcitrant waste products.

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.

Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics

Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights of homologous sequences. However, deciphering the evolutionary variability and complex dependencies among residues presents substantial hurdles. Here, we present a new machine-learning method based on variational autoencoders and evolutionary sampling strategy to address those limitations. We customized our method to generate novel sequences of model enzymes, haloalkane dehalogenases. Three design-build-test cycles improved the solubility of variants from 11% to 75%. Thorough experimental validation including the microfluidic device MicroPEX resulted in 20 multiple-point variants. Nine of them, sharing as little as 67% sequence similarity with the template, showed a melting temperature increase of up to 9 °C and an average improvement of 3 °C. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template. High-quality experimental data collected with 20 variants represent a valuable data set for the critical validation of novel protein design approaches. Python scripts, jupyter notebooks, and data sets are available on GitHub (https://github.com/loschmidt/vae-dehalogenases), and interactive calculations will be possible via https://loschmidt.chemi.muni.cz/fireprotasr/.

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks

Solubility is a key biophysical property of proteins and is essential for evaluating the effectiveness of proteins in biochemical engineering. In recent years, the prediction method of protein solubility has received extensive attention in the protein engineering research community. Many methods have been developed to predict protein solubility, but the generalization performance of existing prediction methods on independent test sets must be improved. In addition, solubility prediction methods do not work well when they are used for regression tasks. To address these issues, we developed a new method, ProG-SOL, an innovative sequence-based dual-graph convolutional network that simultaneously exploits the protein pretrained graph and the protein evolutionary graph for assessing solubility. Compared with other methods, ProG-SOL achieves better classification and regression results for different independent test sets at the same time. The model framework of our method may also be used to predict other properties of proteins such as protein function, protein-protein interaction, protein folding, and drug design, which provide broad application prospects in protein engineering.

Bioinformatics-assisted mining and design of novel pullulanase suitable for starch cold hydrolysis

Cold-active pullulanases with good catalytic performance possess promising applications in cold hydrolysis of starch. Adopting bioinformatics-assisted mining strategies, 7 candidate cold-active pullulanases were initially screened out from IMG/MER database. Among the candidates, PulBs exhibited good thermostability and the highest specific activity of 147.4 U/mg. The half-life of PulBs was about 200 h at 35 °C. Employing PulBs as the initial enzyme, the active-site design of FuncLib was implemented to enhance the activity. The design PulBs-20 exhibited an enhanced specific activity of 209.9 U/mg, which was 1.4 times that of PulBs. Furthermore, the thermostability of PulBs-20 was augmented, with a half-life of 250 h at 35 °C. When applied in the cold hydrolysis of starch, PulBs-20 can effectively enhance the hydrolysis effect of raw starch. Supplemented with the raw starch-hydrolyzing α-amylase AmyZ1 and PulBs-20, the hydrolysis rate of raw corn starch increased to 53.5 %, which was 1.3 times that of using AmyZ1 alone. Due to its high hydrolysis activity and good thermostability, PulBs-20 can serve as an efficient accessory enzyme in starch cold hydrolysis.

Enhancing the Thermostability and solubility of a single-domain catalytic antibody

Catalytic antibodies have the ability to bind to and degrade antigens, offering a significant potential for therapeutic use. The light chain of an antibody, UA15-L, can cleave the peptide bond of Helicobacter pylori urease, thus inhibiting the spread of the bacteria. However, the variable domain of UA15-L has a poor thermostability and solubility. In this study, we employed a combined computational and experimental approach to enhance the protein's stability and solubility properties. The protein unfolding hotspots were initially identified using molecular dynamics simulations. Following this, a disulfide bond was designed in an unfolding hotspot to stabilize the protein. Subsequently, protein solubility was enhanced with the assistance of computational methods by introducing polar or charged residues on the protein surface. The combination of multiple mutations resulted in UA15-L variable domain variants with improved thermostability, solubility, expression, and enhanced activity at elevated temperatures. These variants represent promising candidates for further engineering of catalytic activity and specificity.

Discovery and Synthetic Applications of a NAD(P)H-Dependent Reductive Aminase from Rhodococcus erythropolis

Reductive amination is one of the most synthetically direct routes to access chiral amines. Several Imine Reductases (IREDs) have been discovered to catalyze reductive amination (Reductive Aminases or RedAms), yet they are dependent on the expensive phosphorylated nicotinamide adenine dinucleotide cofactor NADPH and usually more active at basic pH. Here, we describe the discovery and synthetic potential of an IRED from Rhodococcus erythropolis (RytRedAm) that catalyzes reductive amination between a series of medium to large carbonyl and amine compounds with conversions of up to >99% and 99% enantiomeric excess at neutral pH. RytRedAm catalyzes the formation of a substituted γ-lactam and N-methyl-1-phenylethanamine with stereochemistry opposite to that of fungal RedAms, giving the (S)-enantiomer. This enzyme remarkably uses both NADPH and NADH cofactors with K M values of 15 and 247 μM and turnover numbers k cat of 3.6 and 9.0 s-1, respectively, for the reductive amination of hexanal with allylamine. The crystal structure obtained provides insights into the flexibility to also accept NADH, with residues R35 and I69 diverging from that of other IREDs/RedAms in the otherwise conserved Rossmann fold. RytRedAm thus represents a subfamily of enzymes that enable synthetic applications using NADH-dependent reductive amination to access complementary chiral amine products.

Estimating the structural and spatial variables of allantoinase enzyme critical for protein adsorption

Designing enzyme-based sensors necessitates a comprehensive exploration of macromolecular properties. Integrating enzymes with a suitable transducer involves immobilizing them onto a surface, facilitated through adsorption or entrapment techniques. Allantoin, a stable biomarkers metabolite, holds promise for detecting oxidative stress-related complications through its enzyme. In this study, we examined allantoinase from various taxa, with bacterial origin comprising over 70 % of the dataset. Crucial residues such as Asp, His, and Gly in the active binding site and associated hydrophobic area play a critical role in maintaining binding specificity and sensitivity. In this work, we utilized bioinformatics tools to analyze properties such as pI, solubility index, amino acid hydropathy, stability, disordered regions, solvent-accessible surface area, and hydrodynamic parameters. The stability of allantoinase is assessed through surface Cys residues, hydrophobicity, and thermostability. Furthermore, the compactness and spherical geometry of the enzyme, which are crucial for protein adsorption are evaluated through parameters like spatial conformation, asphericity, and hydrodynamic radius distribution. Among the dataset, bacterial allantoinase demonstrates significant adaptability to environmental changes, as indicated by solvent-accessible surface area and instability index. This study highlights the importance of macromolecular properties underscoring their significance in optimizing, calibrating, and ensuring the stability of enzyme-based sensor design.

A review on computational models for predicting protein solubility

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Challenges and strategies in the soluble expression of CTA1-(S14P5)4-DD and CTA1-(S21P2)4-DD fusion proteins as candidates for COVID-19 intranasal vaccines

Developing intranasal vaccines against pandemics and devastating airborne infectious diseases is imperative. The superiority of intranasal vaccines over injectable systemic vaccines is evident, but developing effective intranasal vaccines presents significant challenges. Fusing a protein antigen with the catalytic domain of cholera toxin (CTA1) and the two-domain D of staphylococcal protein A (DD) has significant potential for intranasal vaccines. In this study, we constructed two fusion proteins containing CTA1, tandem repeat linear epitopes of the SARS-CoV-2 spike protein (S14P5 or S21P2), and DD. Structural predictions indicated that each component of the fusion proteins was compatible with its origin. In silico analyses predicted high solubility for both fusion proteins when overexpressed in Escherichia coli. However, contrary to these predictions, the constructs exhibited limited solubility. Lowering the cultivation temperature from 37°C to 18°C did not improve solubility. Inducing expression with IPTG at the early log phase significantly increased soluble CTA1-(S21P2)4-DD but not CTA1-(S14P5)4-DD. Adding non-denaturing detergents (Nonidet P40, Triton X100, or Tween 20) to the extraction buffer significantly enhanced solubility. Despite this, purification experiments yielded low amounts, only 1-2 mg/L of culture, due to substantial losses during the purification stages. These findings highlight the challenges and potential strategies for optimizing soluble expression of CTA1-DD fusion proteins for intranasal vaccines.

In silico design and assessment of a multi-epitope peptide vaccine against multidrug-resistant Acinetobacter baumannii

Acinetobacter baumannii, an opportunistic and notorious nosocomial pathogen, is responsible for many infections affecting soft tissues, skin, lungs, bloodstream, and urinary tract, accounting for more than 722,000 cases annually. Despite the numerous advancements in therapeutic options, no approved vaccine is currently available for this particular bacterium. Consequently, this study focused on creating a rational vaccine design using bioinformatics tools. Three outer membrane proteins with immunogenic potential and properties of good vaccine candidates were used to select epitopes based on good antigenic properties, non-allergenicity, high binding scores, and a low IC50 value. A multi-epitope peptide (MEP) construct was created by sequentially linking the epitopes using suitable linkers. ClusPro 2.0 and C-ImmSim web servers were used for docking analysis with TLR2/TLR4 and immune response respectively. The Ramachandran plot showed an accurate model of the MEP with 100% residue in the most favored and allowed regions. The construct was highly antigenic, stable, non-allergenic, non-toxic, and soluble, and showed maximum population coverage. Additionally, molecular docking demonstrated strong binding between the designed MEP vaccine and TLR2/TLR4. In silico immunological simulations showed significant increases in T-cell and B-cell populations. Finally, codon optimization and in silico cloning were conducted using the pET-28a (+) plasmid vector to evaluate the efficiency of the expression of vaccine peptide in the host organism (Escherichia coli). This designed MEP vaccine would support and accelerate the laboratory work to develop a potent vaccine targeting MDR Acinetobacter baumannii.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00292-3.

Ancestral Sequence Reconstruction for Designing Biocatalysts and Investigating their Functional Mechanisms

Biocatalysis has emerged as a green approach for efficient and sustainable production in various industries. In recent decades, numerous advancements in computational and predictive approaches, including ancestral sequence reconstruction (ASR) have sparked a new wave for protein engineers to improve and expand biocatalyst capabilities. ASR is an evolution-based strategy that uses phylogenetic relationships among homologous extant sequences to probabilistically infer the most likely ancestral sequences. It has proven to be a powerful tool with applications ranging from creating highly stable enzymes for direct applications to preparing moderately active robust protein scaffolds for further enzyme engineering. Intriguingly, it can also provide insights into fundamental aspects that are challenging to study with extant (current) enzymes. This Perspective discusses a practical strategy for guiding enzyme engineers on how to embrace ASR as a practical or associated protocol for protein engineering and highlights recent examples of using ASR in various applications, including increasing thermostability, expanding promiscuity, fine-tuning selectivity and function, and investigating mechanistic and evolution aspects. We believe that the use of the ASR approach will continue to contribute to the ongoing development of the biocatalysis field. We have been in a "golden era" for biocatalysis in which numerous useful enzymes have been developed through many waves of enzyme engineering via advancements in computational methodologies.

GRACE: Generative Redesign in Artificial Computational Enzymology

Designing de novo enzymes is complex and challenging, especially to maintain the activity. This research focused on motif design to identify the crucial domain in the enzyme and uncovered the protein structure by molecular docking. Therefore, we developed a Generative Redesign in Artificial Computational Enzymology (GRACE), which is an automated workflow for reformation and creation of the de novo enzymes for the first time. GRACE integrated RFdiffusion for structure generation, ProteinMPNN for sequence interpretation, CLEAN for enzyme classification, and followed by solubility analysis and molecular dynamic simulation. As a result, we selected two gene sequences associated with carbonic anhydrase from among 10,000 protein candidates. Experimental validation confirmed that these two novel enzymes, i.e., dCA12_2 and dCA23_1, exhibited favorable solubility, promising substrate-active site interactions, and achieved activity of 400 WAU/mL. This workflow has the potential to greatly streamline experimental efforts in enzyme engineering and unlock new avenues for rational protein design.

Design and computational analysis of a novel Azurin-BR2 chimeric protein against breast cancer

Cancer is one of most lethal diseases worldwide. Chemotherapeutics and surgeries are among the treatment facilities available for curing cancer. However due to their negative impact on normal cells and drug resistance development, new treatment strategies have yet to be developed. Some microbial products exhibit therapeutic potential for treating cancer. Pseudomonas aeruginosa Azurins have shown anticancer effects against breast cancer without affecting normal cells. To enhance its cytotoxic effect and targeted delivery, we fused Azurin with a cell-penetrating peptide (BR2) through a rigid linker and evaluated its anticancer potential via in silico analysis. The prediction of the secondary and the tertiary structures and analysis of physiochemical properties of chimeric proteins were computationally performed. The Azurin-BR2 chimeric protein has a basic nature with a molecular weight of 16.8 kDa. The quality indices and validation of chimeric proteins were performed with ERRAT2 and Ramachandran plot values, respectively. The quality index of the chimeric protein was predicted to be 81% to 84.6%, and residues residing in the most favoured region were identified. The HDOCK bioinformatics tool was used for docking a chimeric protein with a cancer suppressor protein p53. The results of the current study support that an Azurin-BR2 fusion protein has a high binding affinity for p53 can induce apoptosis in cancerous cells, and can be used in tumor-targeting therapy.

Isolation of anti-Ancylostoma-secreted protein 5 (ASP5) antibody from a naïve antibody phage library

Ancylostoma species are parasitic nematodes that release a multitude of proteins to manipulate host immune responses to facilitate their survival. Among the released proteins, Ancylostoma-secreted protein 5 (ASP5) plays a pivotal role in mediating host-parasite interactions, making it a promising target for interventions against canine hookworm infections caused by Ancylostoma species. Antibody phage display, a widely used method for generating human monoclonal antibodies was employed in this study. A bacterial expression system was used to produce ASP5 for biopanning. A single-chain fragment variable (scFv) monoclonal antibody against ASP5 was generated from the naïve Human AntibodY LibrarY (HAYLY). The resulting scFv antibody was characterized to elucidate its antigen-binding properties. The identified monoclonal antibody showed good specificity and binding characteristics which highlights its potential for diagnostic applications for hookworm infections.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli

Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.

Reconstituted Cell-free Translation Systems for Exploring Protein Folding and Aggregation

Protein folding is crucial for achieving functional three-dimensional structures. However, the process is often hampered by aggregate formation, necessitating the presence of chaperones and quality control systems within the cell to maintain protein homeostasis. Despite a long history of folding studies involving the denaturation and subsequent refolding of translation-completed purified proteins, numerous facets of cotranslational folding, wherein nascent polypeptides are synthesized by ribosomes and folded during translation, remain unexplored. Cell-free protein synthesis (CFPS) systems are invaluable tools for studying cotranslational folding, offering a platform not only for elucidating mechanisms but also for large-scale analyses to identify aggregation-prone proteins. This review provides an overview of the extensive use of CFPS in folding studies to date. In particular, we discuss a comprehensive aggregation formation assay of thousands of Escherichia coli proteins conducted under chaperone-free conditions using a reconstituted translation system, along with its derived studies.

Identification and characterization of a novel thermostable PL7 alginate lyase from a submarine volcanic metagenomic library

Seaweed biomass is as an abundant and renewable source of complex polysaccharides, including alginate which has a variety of applications. A sustainable method for exploiting alginate towards the production of valuable oligosaccharides is through enzymatic processing, using alginate lyases. Industrial refinement methods demand robust enzymes. Metagenomic libraries from extreme environments are a new source of unique enzymes with great industrial potential. Herein we report the identification of a new thermostable alginate lyase with only 58 % identity to known sequences, identified by mining a metagenomic library obtained from the hydrothermal vents of the volcano Kolumbo in the Aegean Sea (Kolumbo Alginate Lyase, KAlLy). Sequence analysis and biochemical characterization of KAlLy showed that this new alginate lyase is a Polysaccharide Lyase of family 7 (PL7) enzyme with endo- and exo-action on alginate and poly-mannuronic acid, with high activity at 60°C (56 ± 8 U/mg) and high thermostability (half-life time of 30 h at 50°C). The response surface methodology analysis revealed that the reaction optimum conditions with poly-mannuronic acid as substrate are 44°C, pH of 5.5 with 440 mM NaCl. This novel alginate lyase is a valuable addition to the toolbox of alginate modifying enzymes, due to its diverse sequence and its good thermal stability.

ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution

Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi.

Deep Intraclonal Analysis for the Development of Vaccines against Drug-Resistant Klebsiella pneumoniae Lineages

Despite its medical relevance, there is no commercial vaccine that protects the population at risk from multidrug-resistant (MDR) Klebsiella pneumoniae infections. The availability of massive omic data and novel algorithms may improve antigen selection to develop effective prophylactic strategies. Up to 133 exposed proteins in the core proteomes, between 516 and 8666 genome samples, of the six most relevant MDR clonal groups (CGs) carried conserved B-cell epitopes, suggesting minimized future evasion if utilized for vaccination. Antigens showed a range of epitopicity, functional constraints, and potential side effects. Eleven antigens, including three sugar porins, were represented in all MDR-CGs, constitutively expressed, and showed limited reactivity with gut microbiota. Some of these antigens had important interactomic interactions and may elicit adhesion-neutralizing antibodies. Synergistic bivalent to pentavalent combinations that address expression conditions, interactome location, virulence activities, and clone-specific proteins may overcome the limiting protection of univalent vaccines. The combination of five central antigens accounted for 41% of all non-redundant interacting partners of the antigen dataset. Specific antigen mixtures represented in a few or just one MDR-CG further reduced the chance of microbiota interference. Rational antigen selection schemes facilitate the design of high-coverage and "magic bullet" multivalent vaccines against recalcitrant K. pneumoniae lineages.

Enhancing the expression of terminal deoxynucleotidyl transferases by N-terminal truncation

Terminal deoxynucleotidyl transferase (TdT), a unique DNA polymerase that catalyzes the template-free incorporation of nucleotides into single-stranded DNA, has facilitated the development of various oligonucleotide-based tools and methods, especially in the field of template-free enzymatic DNA synthesis. However, expressing vertebrate-derived TdTs in Escherichia coli complicates purification and increases production costs. In this study, N-terminal truncation of TdTs was performed to improve their expression and stability. The results revealed that N-terminal truncation could enhance the expression level of six TdTs. Among the truncated mutants, N-140-ZaTdT and N-140-CpTdT, with 140 amino acids removed, exhibited an increase in protein expression, which was 9.5- and 23-fold higher than their wild-types, respectively. Importantly, the truncation preserves the catalytic function of TdT. Additionally, the Tm values of N-140-ZaTdT increased by 4.9°C. The improved expression of the truncated mutants makes them more suitable for reducing production costs and advancing enzyme engineering.

High-level expression of codon-optimized Taq DNA polymerase under the control of rhaBAD promoter

A DNA polymerase from Thermus aquaticus remains the most popular among DNA polymerases. It was widely applied in various fields involving the application of polymerase chain reaction (PCR), implying the high commercial value of this enzyme. For this reason, an attempt to obtain a high yield of Taq DNA polymerase is continuously conducted. In this study, the l-rhamnose-inducible promoter rhaBAD was utilized due to its ability to produce recombinant protein under tight control in E. coli expression system. Instead of full-length Taq polymerase, an N-terminal deletion of Taq polymerase was selected. To obtain a high-level expression, we attempted to optimize the codon by reducing the rare codon and GC content, and in a second attempt, we optimized the culture conditions for protein expression. The production of Taq polymerase using the optimum culture condition improved the level of expression by up to 3-fold. This approach further proved that a high level of recombinant protein expression could be achieved by yielding a purified Taq polymerase of about 8.5 mg/L of culture. This is the first research publication on the production of Taq polymerase with N-terminal deletion in E. coli with the control of the rhaBAD promoter system.

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

In Silico Analysis and Development of the Secretory Expression of D-Psicose-3-Epimerase in Escherichia coli

D-psicose-3-epimerase (DPEase), a key enzyme for D-psicose production, has been successfully expressed in Escherichia coli with high yield. However, intracellular expression results in high downstream processing costs and greater risk of lipopolysaccharide (LPS) contamination during cell disruption. The secretory expression of DPEase could minimize the number of purification steps and prevent LPS contamination, but achieving the secretion expression of DPEase in E. coli is challenging and has not been reported due to certain limitations. This study addresses these challenges by enhancing the secretion of DPEase in E. coli through computational predictions and structural analyses. Signal peptide prediction identified PelB as the most effective signal peptide for DPEase localization and enhanced solubility. Supplementary strategies included the addition of 0.1% (v/v) Triton X-100 to promote protein secretion, resulting in higher extracellular DPEase (0.5 unit/mL). Low-temperature expression (20 °C) mitigated the formation of inclusion bodies, thus enhancing DPEase solubility. Our findings highlight the pivotal role of signal peptide selection in modulating DPEase solubility and activity, offering valuable insights for protein expression and secretion studies, especially for rare sugar production. Ongoing exploration of alternative signal peptides and refinement of secretion strategies promise further enhancement in enzyme secretion efficiency and process safety, paving the way for broader applications in biotechnology.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

Bioinformatics-Driven mRNA-Based Vaccine Design for Controlling Tinea Cruris Induced by Trichophyton rubrum

Tinea cruris, a dermatophyte fungal infection predominantly caused by Trichophyton rubrum and Epidermophyton floccosum, primarily affects the groin, pubic region, and adjacent thigh. Its recurrence is frequent, attributable to repeated fungal infections in susceptible individuals, especially those with onychomycosis or tinea pedis, which act as reservoirs for dermatophytes. Given the persistent nature of tinea cruris, vaccination emerges as a promising strategy for fungal infection management, offering targeted, durable protection against various fungal species. Vaccines stimulate both humoral and cell-mediated immunity and are administered prophylactically to prevent infections while minimizing the risk of antifungal resistance development. Developing fungal vaccines is challenging due to the thick fungal cell wall, similarities between fungal and human cells, antigenic variation, and evolutionary resemblance to animals, complicating non-toxic target identification and T-cell response variability. No prior research has shown an mRNA vaccine for T. rubrum. Hence, this study proposes a novel mRNA-based vaccine for tinea cruris, potentially offering long-term immunity and reducing reliance on antifungal medications. This study explores the complete proteome of T. rubrum, identifying potential protein candidates for vaccine development through reverse vaccinology. Immunogenic epitopes from these candidates were mapped and integrated into multitope vaccines and reverse translated to construct mRNA vaccines. Then, the mRNA was translated and computationally assessed for physicochemical, chemical, and immunological attributes. Notably, 1,3-beta-glucanosyltransferase, CFEM domain-containing protein, cell wall galactomannoprotein, and LysM domain-containing protein emerged as promising vaccine targets. Antigenic, immunogenic, non-toxic, and non-allergenic cytotoxic T lymphocyte, helper T lymphocyte, and B lymphocyte epitopes were selected and linked with appropriate linkers and Toll-like receptor (TLR) agonist adjuvants to formulate vaccine candidates targeting T. rubrum. The protein-based vaccines underwent reverse translation to construct the mRNA vaccines, which, after inoculation, were translated again by host ribosomes to work as potential components for triggering the immune response. After that, molecular docking, normal mode analysis, and molecular dynamic simulation confirmed strong binding affinities and stable complexes between vaccines and TLR receptors. Furthermore, immune simulations of vaccines with and without adjuvant demonstrated activation of immune responses, evidenced by elevated levels of IgG1, IgG2, IgM antibodies, cytokines, and interleukins. There was no significant change in antibody production between vaccines with and without adjuvants, but adjuvants are crucial for activating the innate immune response via TLRs. Although mRNA vaccines hold promise against fungal infections, further research is essential to assess their safety and efficacy. Experimental validation is crucial for evaluating their immunogenicity, effectiveness, and safety.

PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

GPSFun: geometry-aware protein sequence function predictions with language models

Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.

The Development and Opportunities of Predictive Biotechnology

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Integrating pan-genome and reverse vaccinology to design multi-epitope vaccine against Herpes simplex virus type-1

Herpes simplex virus type-1 (HSV-1), the etiological agent of sporadic encephalitis and recurring oral (sometimes genital) infections in humans, affects millions each year. The evolving viral genome reduces susceptibility to existing antivirals and, thus, necessitates new therapeutic strategies. Immunoinformatics strategies have shown promise in designing novel vaccine candidates in the absence of a clinically licensed vaccine to prevent HSV-1. However, to encourage clinical translation, the HSV-1 pan-genome was integrated with the reverse-vaccinology pipeline for rigorous screening of universal vaccine candidates. Viral targets were screened from 104 available complete genomes. Among 364 proteins, envelope glycoprotein D being an outer membrane protein with a high antigenicity score (> 0.4) and solubility (> 0.6) was selected for epitope screening. A total of 17 T-cell and 4 B-cell epitopes with highly antigenic, immunogenic, non-toxic properties and high global population coverage were identified. Furthermore, 8 vaccine constructs were designed using different combinations of epitopes and suitable linkers. VC-8 was identified as the most potential vaccine candidate regarding chemical and structural stability. Molecular docking revealed high interactive affinity (low binding energy: - 56.25 kcal/mol) of VC-8 with the target elicited by firm intermolecular H-bonds, salt-bridges, and hydrophobic interactions, which was validated with simulations. Compatibility of the vaccine candidate to be expressed in pET-29(a) + plasmid was established by in silico cloning studies. Immune simulations confirmed the potential of VC-8 to trigger robust B-cell, T-cell, cytokine, and antibody-mediated responses, thereby suggesting a promising candidate for the future of HSV-1 prevention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04022-6.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

GASP: A Pan-Specific Predictor of Family 1 Glycosyltransferase Acceptor Specificity Enabled by a Pipeline for Substrate Feature Generation and Large-Scale Experimental Screening

Glycosylation represents a major chemical challenge; while it is one of the most common reactions in Nature, conventional chemistry struggles with stereochemistry, regioselectivity, and solubility issues. In contrast, family 1 glycosyltransferase (GT1) enzymes can glycosylate virtually any given nucleophilic group with perfect control over stereochemistry and regioselectivity. However, the appropriate catalyst for a given reaction needs to be identified among the tens of thousands of available sequences. Here, we present the glycosyltransferase acceptor specificity predictor (GASP) model, a data-driven approach to the identification of reactive GT1:acceptor pairs. We trained a random forest-based acceptor predictor on literature data and validated it on independent in-house generated data on 1001 GT1:acceptor pairs, obtaining an AUROC of 0.79 and a balanced accuracy of 72%. The performance was stable even in the case of completely new GT1s and acceptors not present in the training data set, highlighting the pan-specificity of GASP. Moreover, the model is capable of parsing all known GT1 sequences, as well as all chemicals, the latter through a pipeline for the generation of 153 chemical features for a given molecule taking the CID or SMILES as input (freely available at https://github.com/degnbol/GASP). To investigate the power of GASP, the model prediction probability scores were compared to GT1 substrate conversion yields from a newly published data set, with the top 50% of GASP predictions corresponding to reactions with >50% synthetic yields. The model was also tested in two comparative case studies: glycosylation of the antihelminth drug niclosamide and the plant defensive compound DIBOA. In the first study, the model achieved an 83% hit rate, outperforming a hit rate of 53% from a random selection assay. In the second case study, the hit rate of GASP was 50%, and while being lower than the hit rate of 83% using expert-selected enzymes, it provides a reasonable performance for the cases when an expert opinion is unavailable. The hierarchal importance of the generated chemical features was investigated by negative feature selection, revealing properties related to cyclization and atom hybridization status to be the most important characteristics for accurate prediction. Our study provides a GT1:acceptor predictor which can be trained on other data sets enabled by the automated feature generation pipelines. We also release the new in-house generated data set used for testing of GASP to facilitate the future development of GT1 activity predictors and their robust benchmarking.

A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening

Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BACKGROUND

Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.

RESULTS

In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.

CONCLUSIONS

GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Discovery of Latent Cannabichromene Cyclase Activity in Marine Bacterial Flavoenzymes

Production of phytocannabinoids remains an area of active scientific interest due to the growing use of cannabis by the public and the underexplored therapeutic potential of the over 100 minor cannabinoids. While phytocannabinoids are biosynthesized by Cannabis sativa and other select plants and fungi, structural analogs and stereoisomers can only be accessed synthetically or through heterologous expression. To date, the bioproduction of cannabinoids has required eukaryotic hosts like yeast since key, native oxidative cyclization enzymes do not express well in bacterial hosts. Here, we report that two marine bacterial flavoenzymes, Clz9 and Tcz9, perform oxidative cyclization reactions on phytocannabinoid precursors to efficiently generate cannabichromene scaffolds. Furthermore, Clz9 and Tcz9 express robustly in bacteria and display significant tolerance to organic solvent and high substrate loading, thereby enabling fermentative production of cannabichromenic acid in Escherichia coli and indicating their potential for biocatalyst development.

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.