Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility

Insulin fibrillation under physicochemical parameters of bioprocessing and intervention by peptides and surface-active agents

Even after the centenary celebration of insulin discovery, there prevail challenges concerning insulin aggregation, not only after repeated administration but also during industrial production, storage, transport, and delivery, significantly impacting protein quality, efficacy, and effectiveness. The aggregation reduces insulin bioavailability, increasing the risk of heightened immunogenicity, posing a threat to patient health, and creating a dent in the golden success story of insulin therapy. Insulin experiences various physicochemical and mechanical stresses due to modulations in pH, temperature, ionic strength, agitation, shear, and surface chemistry, during the upstream and downstream bioprocessing, resulting in insulin unfolding and subsequent fibrillation. This has fueled research in the pharmaceutical industry and academia to unveil the mechanistic insights of insulin aggregation in an attempt to devise rational strategies to regulate this unwanted phenomenon. The present review briefly describes the impacts of environmental factors of bioprocessing on the stability of insulin and correlates with various intermolecular interactions, particularly hydrophobic and electrostatic forces. The aggregation-prone regions of insulin are identified and interrelated with biophysical changes during stress conditions. The quest for novel additives, surface-active agents, and bioderived peptides in decelerating insulin aggregation, which results in overall structural stability, is described. We hope this review will help tackle the real-world challenges of insulin aggregation encountered during bioprocessing, ensuring safer, stable, and globally accessible insulin for efficient management of diabetes.

Cross-species analysis of the nuclease Artemis highlights its evolving function in domesticating RAG-like transposons and residues that are crucial for activity

The discovery of the ProtoRAG transposon in lancelets revealed that V(D)J recombination originates from the Recombination activating gene-like (RAGL) transposon. Analogous to the vertebrate RAG complex, the RAGL transposase nicks host flanking DNA and leads to the formation of hairpin ends. Here, we showed that the Artemis nuclease, which is capable of resolving DNA hairpin ends generated during V(D)J recombination, is also responsible for unraveling ProtoRAG-mediated DNA hairpin ends. Notably, like the RAGL transposon, Artemis originated from the eukaryotic common ancestor. By tracing the evolving function of Artemis from cephalochordates to vertebrates, we revealed the lineage specific allele polymorphism of lancelet Artemis and uncovered an increased activity on hairpin DNA opening in vertebrate Artemis. Additionally, the evolutionarily conserved LYCS motif in Artemis β6, which may be associated with disease, is demonstrated to be crucial for its function. Overall, this study highlights the evolving function of Artemis, identifies novel critical residues, and provides new insights into the evolution of RAG-mediated recombination and the clinical therapy of Artemis deficient disease.

Insight Into Factors Influencing the Aggregation Process in Wild-Type and P66R Mutant SOD1: Computational and Spectroscopic Approaches

Disturbances in metal ion homeostasis associated with amyotrophic lateral sclerosis (ALS) have been described for several years, but the exact mechanism of involvement is not well understood. To elucidate the role of metalation in superoxide dismutase (SOD1) misfolding and aggregation, we comprehensively characterized the structural features (apo/holo forms) of WT-SOD1 and P66R mutant in loop IV. Using computational and experimental methodologies, we assessed the physicochemical properties of these variants and their correlation with protein aggregation at the molecular level. Modifications in apo-SOD1 compared to holo-SOD1 were more pronounced in flexibility, stability, hydrophobicity, and intramolecular interactions, as indicated by molecular dynamics simulations. The enzymatic activities of holo/apo-WT SOD1 were 1.30 and 1.88-fold of the holo/apo P66R mutant, respectively. Under amyloid-inducing conditions, decreased ANS fluorescence intensity in the apo-form relative to the holo-form suggested pre-fibrillar species and amyloid aggregate growth due to occluded hydrophobic pockets. FTIR spectroscopy revealed that apo-WT-SOD1 and apo-P66R exhibited a mixture of parallel and intermolecular β-sheet structures, indicative of aggregation propensity. Aggregate species were identified using TEM, Congo red staining, and ThT/ANS fluorescence spectroscopy. Thermodynamic analyses with GdnHCl demonstrated that metal deficit, mutation, and intramolecular disulfide bond reduction are essential for initiating SOD1 misfolding and aggregation. These disruptions destabilize the dimer-monomer equilibrium, promoting dimer dissociation into monomers and decreasing the thermodynamic stability of SOD1 variants, thus facilitating amyloid/amorphous aggregate formation. Our findings offer novel insights into protein aggregation mechanisms in disease pathology and highlight potential therapeutic strategies against toxic protein aggregation, including SOD1.

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Computational investigation of antiviral peptide interactions with Mpox DNA polymerase

The Mpox DNA polymerase (DNA pol) plays a crucial role in the viral replication process, making it an ideal target for antiviral therapies. It facilitates the synthetic process of viral DNA, which is an integral stage in the life of a virus. The inhibition of the operation of Mpox DNA pol would interfere with the multiplication of the virus and help manage the disease. Peptides have emerged as a possible therapeutic alternative against viruses due to their distinct characteristics. Peptides have broad-spectrum antiviral activity, being effective against a variety of viruses. Using computational techniques, we attempted to explore the molecular details of the interaction between antiviral peptides and Mpox DNA pol. Two databases of antiviral peptides were screened in this study. This study used molecular docking, followed by molecular dynamics (MD) simulation and post-simulation binding energy predictions. From the 19 selected peptides with activity against DNA polymerases, two peptides-DRAVPe01393 and DRAVPe01399-were identified as particularly promising candidates. These peptides exhibited stable interactions with Mpox DNA pol and demonstrated good cell penetration potential as evident from the MD simulation studies. Notably, the peptides DRAVPe01399 and DRAVPe01393 have a better binding affinity of - 60.86 kcal/mol and - 47.92 kcal/mol respectively than the control ligand Cidofovir diphosphate (- 10.79 kcal/mol). These findings could lead to the development of innovative antiviral treatments to prevent monkeypox, helping global efforts to battle this emerging infectious disease.

A Practical Guide to Computational Tools for Engineering Biocatalytic Properties

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate enzyme discovery. With a plethora of software and tools available, researchers from different disciplines often face challenges in selecting the most suitable method that meets their requirements and available starting data. This review categorizes the computational tools available for enzyme engineering based on their capacity to enhance the following specific biocatalytic properties of biotechnological interest: (i) protein-ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, and (iv) solubility for recombinant enzyme production. By aligning tools with their respective scoring functions, we aim to guide researchers, particularly those new to computational methods, in selecting the appropriate software for the design of protein engineering campaigns. De novo enzyme design, involving the creation of novel proteins, is beyond this review's scope. Instead, we focus on practical strategies for fine-tuning enzymatic performance within an established reference framework of natural proteins.

Aggregation-prone antimicrobial peptides target gram-negative bacterial nucleic acids and protein synthesis

Aggregation of antimicrobial peptides (AMPs) enhances their efficacy by destabilising the bacterial cell wall, membrane, and cytosolic proteins. Developing aggregation-prone AMPs offers a promising strategy to combat antibiotic resistance, though predicting such AMPs and understanding bacterial responses remain challenging. Octopus bimaculoides, a cephalopod species, lacks known AMP gene families, yet its protein fragments were used to predict AMPs via artificial intelligence tools. Four peptides (Oct-P1, Oct-P2, Oct-P3, and Oct-P4) were identified based on their aggregation propensity. Among them, Oct-P2 reduced the viability of Escherichia coli and Staphylococcus aureus by up to 90 %, confirmed by confocal laser scanning microscopy and scanning electron microscopy. It further aggregated plasmid DNA in vitro, and the presence of extracellular DNA reduced their antibacterial activity. With knockout mutants, it revealed that Oct-P2 was internalized into bacterial cells, possibly through membrane transport proteins, enhancing its antibacterial effect. Aggregation-induced emission assays and molecular dynamics simulations revealed that Oct-P2 aggregates with transcription promoter DNA, inhibiting transcription and translation in vitro. This dual-target mechanism not only highlights the potential of Oct-P2 as a lead template for new antimicrobial drug development, but also opens a new window for discovering AMPs from protein fragments against the upcoming challenge of bacterial infections. STATEMENT OF SIGNIFICANCE: A popular strategy for identifying antimicrobial peptides (AMPs) in specific genomes uses the conserved regions of AMP families, but this strategy has limitations in organisms lacking classical AMP gene families, such as Octopus. Fragments from non-antimicrobial proteins serve as a rich source for the identification of new AMPs. In this study, we used artificial intelligence tools to search for potential candidate AMP sequences from non-antimicrobial proteins in Octopus bimaculoides. The successful identification of aggregation-prone AMPs was shown to decrease bacterial viability, increase permeability, and reduce biomass. One candidate, Oct-P2, kills the gram-negative bacteria E. coli by aggregating with DNA and inhibiting transcription and translation, suggesting a new intracellular mechanism of AMP activity.

Identification and expression of a hydrophobic carboxylic acid reductase from Trichoderma virens

Carboxylic acid reductases (CARs) have been garnering attention in applications for the sustainable synthesis of aldehydes. Despite numerous discoveries, not all characteristics of CAR enzymes have been extensively studied or understood. Herein, we report the discovery and expression of a new CAR enzyme (TvirCAR2) from the ascomycetous fungus, Trichoderma virens. Tvircar2 is one of the five putative CARs identified from analyses of the T. virens genome. In silico, analyses showed that TvirCAR2 has a high hydrophobicity index and that its corresponding gene is part of a biosynthetic gene cluster predicted to synthesize hybrid polyketide synthases-nonribosomal peptide synthetase secondary metabolites. TvirCAR2 was highly expressed as soluble and insoluble forms in an Escherichia coli expression host. The solubility of the purified TvirCAR2 necessitated the addition of glycerol in the purification and assay buffers. Substrate screening via molecular docking showed that benzoic acid was a suitable substrate candidate. The TvirCAR2 enzyme catalyzed the reduction of benzoic acid with a specific activity of around 1.4 µmol/h/mg. Homologs, which are predicted to exhibit similar hydrophobicity, are the CARs from Stachybotrys bisbyi (StbB), which is involved in the production of the meroterpenoid, ilicicolin B, and Trichoderma reesei (TrCAR), which is part of a similar but still uncharacterized biosynthetic gene cluster.

Prediction and Evaluation of Protein Aggregation with Computational Methods

Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.

Introducing glutamic acid residues to acyl-ACP reductase to enhance alka(e)ne production in Escherichia coli: Computer-aided design and subsequent experimental validation

Acyl-acyl carrier protein (acyl-ACP) reductase (AAR) is a crucial enzyme in alka(e)ne production by recombinant Escherichia coli (E. coli). Engineered AAR expressed in E. coli holds great promise for the production of alka(e)nes, which are a valuable bio-based alternative to fossil fuels. However, its effectiveness is significantly limited by its low solubility and stability. The aim of this study is to enhance the solubility and stability of AAR to improve the production of alka(e)nes in E. coli. In this study, an integrated computational approach was employed for combining solubility prediction, aggregation propensity prediction, structural modeling, and molecular dynamics (MD) simulations. This multi-faceted approach provides new insights and tools for enzyme engineering. Through this approach, the C-terminus of AAR was identified as the sole significant hydrophobic patch and aggregation-prone regions (APR). Three strategies were evaluated experimentally: direct deletion of these hydrophobic residues; substitution of these residues with negatively charged amino acids, such as glutamic acid (Glu) or aspartic acid (Asp); and the introduction of additional negatively charged amino acids at the C-terminus to shield the hydrophobic patches. The results showed that AAR mutants with additional Glu residues at the C-terminus exhibited improved performance. Specifically, the AAR-E3 mutant, containing three consecutive Glu residues, demonstrated significantly enhanced solubility and stability, with alka(e)ne production (159.25 mg/L) being 6.3 times higher than that of the wild-type AAR (25.37 mg/L). Subsequent computational modeling and molecular dynamics simulations further validated the experimental findings. This study highlights the potential of enzyme engineering to significantly enhance biofuel production efficiency.

Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline

The successful progression of therapeutic antibodies and other biologics from the laboratory to the clinic depends on their possession of "drug-like" biophysical properties. The techniques and the resultant biophysical and biochemical parameters used to characterize their ease of manufacture can be broadly defined as developability. Focusing on antibodies, this review firstly discusses established and emerging biophysical techniques used to probe the early-stage developability of biologics, aimed towards those new to the field. Secondly, we describe the inter-relationships and redundancies amongst developability assays and how in silico methods aid the efficient deployment of developability to bring a new generation of cost-effective therapeutic proteins from bench to bedside more quickly and sustainably.

Aggregating amyloid resources: A comprehensive review of databases on amyloid-like aggregation

Protein aggregation is responsible for several degenerative conditions in humans, and it is also a bottleneck in industrial protein production and storage of biotherapeutics. Bioinformatics tools have been developed to predict and redesign protein solubility more efficiently by understanding the underlying principles behind aggregation. As more experimental data become available, dedicated resources for storing, indexing, classifying and consolidating experimental results have emerged. These resources vary in focus, including aggregation-prone regions, 3D patches or protein stretches capable of forming amyloid fibrils. Some of these resources also consider the experimental conditions that cause protein aggregation and how they affect the process. This review article explores how protein aggregation databases have evolved and surveys state-of-the-art resources. We highlight their applications, complementarity and existing limitations. Moreover, we showcase the existing symbiosis between amyloid-related databases and predictive tools. To increase the usefulness of our review, we supplement it with a comprehensive list of present and past amyloid databases: https://biogenies.info/amyloid-database-list/.

Comprehensive in silico analysis of single nucleotide polymorphism and molecular dynamics simulation of human GATA6 protein in ventricular septal defect

Congenital heart disease (CHD) represents nearly one-third of congenital birth defects annually, with ventricular septal defect (VSD) being the most common type. The aim of this study was to explore the role of specific GATA binding protein 6 gene (GATA6) mutations as a potential etiological factor in the development of VSD through an in silico approach. Data were collected from the human gene databases: DisGeNET and GeneCards, with protein-protein interaction networks constructed via STRING and Cytoscape. Gene ontology and pathway enrichment analyses were conducted using DAVID, with data analysis in R with significance set at FDR p<0.05. Target single nucleotide polymorphisms (SNPs) of GATA6 were obtained from NCBI dbSNP, and non- synonymous single nucleotide polymorphism (nsSNP) effects were predicted using SIFT, PolyPhen-2, I-Mutant 2.0, Fathmm, MutPred 2.0, SNP&GO, and PON-P2. Conserved regions of GATA6 were analyzed using ConSurf, with functional classification, variant conservation, and stability changes evaluated in Google Colab. Multiple sequence alignment was performed using ClustalW. Mutation modeling and molecular dynamics analysis, using GROMACS, revealed that among 87 intersecting genes, 16 proteins were interconnected with GATA6, showing a centrality value of 0.4378. Gene ontology analysis highlighted atrioventricular canal development, protein-DNA complexes, and transcription factor regulation as key processes for cardiac development, especially in the ventricular septum. NsSNP and molecular dynamics analyses identified rs387906818 and rs387906820 as having the highest pathogenic potential for VSD due to amino acid structural changes.

Second transplantation after kidney graft loss in primary hyperoxaluria type 2: a pedigree study and mutation analysis

BACKGROUND

Primary hyperoxaluria type 2 (PH2) is a rare disorder caused by GRHPR mutations. Research on the mutation spectrum and pedigree of PH2 helps in comprehending its pathogenesis and clinical outcomes, guiding clinical diagnosis and treatment.

METHODS

We report a case of PH2 with a three-generational pedigree. The GRHPR genotypes of the family members were confirmed by Sanger sequencing. Urine and blood samples were collected for biochemical analysis. Computational analysis was performed to assess the pathogenicity of the mutations. Cellular experiments based on site-directed mutagenesis were conducted to confirm the effect of mutations on GRHPR expression, activity, and subcellular localization.

RESULTS

The proband underwent her first kidney transplantation in 2015, and experienced recurrent urinary tract infections and urolithiasis postoperatively. Graft failure occurred in 2018. Whole exome sequencing identified compound heterozygous GRHPR mutations p.G160E/p.P203Rfs*7. The patient underwent a second kidney transplantation in 2019 and maintained good graft function with urine dilution measures. Notably, her brother and sister carried the same mutations; however, only the proband progressed to renal failure. Computational analysis suggested that p.G160E reduced the affinity of GRHPR for coenzymes. Cellular experiments indicated that p.G160E reduced GRHPR activity (p < 0.001), whereas p.P203Rfs*7 not only suppressed expression (p < 0.001) and reduced activity (p < 0.001), but also facilitated protein aggregation. Based on our results, the variant p.G160E was classified as 'pathogenic' according to ACMG guidelines.

CONCLUSIONS

Our findings suggest that treatment strategies for the long-term prevention of oxalate nephropathy should be developed for patients with PH2 receiving isolated kidney transplantation. Moreover, the pathogenicity of the compound heterozygous GRHPR mutations p.G160E/p.P203Rfs*7 was also validated.

Rapid Coaggregation of Proteins Without Sequence Similarity: Possible Role of Conformational Complementarity

Despite extensive research on the sequence-determined self-assembly of both pathogenic and nonpathogenic proteins, the question of how the sequence identity would influence the coassembly or cross-seeding of diverse proteins without distinct sequence similarity remains largely unanswered. Here, we demonstrate that the rapid coaggregation of proteins with negligible sequence similarity is fundamentally governed by preferred heteromeric interactions between their partially unfolded states via the gain of additional charge complementarity and hydrophobic interactions. The partial loss of intramolecular interactions and concurrent gain of non-native intrinsically disordered regions with sticky groups become crucial for both aggressive heteromeric primary nucleation and secondary nucleation events. The results signify the direct relevance of sequence-independent conformational cross-talk between diverse proteins to the foundational events required for the growth of biological multiprotein amyloid deposits.

Inhibitory effects of extracts from Eucalyptus gunnii on α-synuclein amyloid fibrils

Amyloid fibril formation is associated with various amyloidoses, including neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Despite the numerous studies on the inhibition of amyloid formation, the prevention and treatment of a majority of amyloid-related disorders are still challenging. In this study, we investigated the effects of various plant extracts on amyloid formation of α-synuclein. We found that the extracts from Eucalyptus gunnii are able to inhibit amyloid formation, and to disaggregate preformed fibrils, in vitro. The extract itself did not lead to cell damage. In the extract, miquelianin, which is a glycosylated form of quercetin and has been detected in the plasma and the brain, was identified and assessed to have a moderate inhibitory activity, compared to the effects of ellagic acid and quercetin, which are strong inhibitors for amyloid formation. The properties of miquelianin provide insights into the mechanisms controlling the assembly of α-synuclein in the brain.

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.

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here, we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the Catalog of Somatic Mutations In Cancer database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Natural compound plumbagin based inhibition of hIAPP revealed by Markov state models based on MD data along with experimental validations

Human islet amyloid polypeptide (amylin or hIAPP) is a 37 residue hormone co-secreted with insulin from β cells of the pancreas. In patients suffering from type-2 diabetes, amylin self-assembles into amyloid fibrils, ultimately leading to the death of the pancreatic cells. However, a research gap exists in preventing and treating such amyloidosis. Plumbagin, a natural compound, has previously been demonstrated to have inhibitory potential against insulin amyloidosis. Our investigation unveils collapsible regions within hIAPP that, upon collapse, facilitates hydrophobic and pi-pi interactions, ultimately leading to aggregation. Intriguingly plumbagin exhibits the ability to bind these specific collapsible regions, thereby impeding the aforementioned interactions that would otherwise drive hIAPP aggregation. We have used atomistic molecular dynamics approach to determine secondary structural changes. MSM shows metastable states forming native like hIAPP structure in presence of PGN. Our in silico results concur with in vitro results. The ThT assay revealed a striking 50% decrease in fluorescence intensity at a 1:1 ratio of hIAPP to Plumbagin. This finding suggests a significant inhibition of amyloid fibril formation by plumbagin, as ThT fluorescence directly correlates with the presence of these fibrils. Further TEM images revealed disappearance of hIAPP fibrils in plumbagin pre-treated hIAPP samples. Also, we have shown that plumbagin disrupts the intermolecular hydrogen bonding in hIAPP fibrils leading to an increase in the average beta strand spacing, thereby causing disaggregation of pre-formed fibrils demonstrating overall disruption of the aggregation machinery of hIAPP. Our work is the first to report a detailed atomistic simulation of 22 μs for hIAPP. Overall, our studies put plumbagin as a potential candidate for both preventive and therapeutic candidate for hIAPP amyloidosis.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Design and characterization of a multi-epitope vaccine targeting Chlamydia abortus using immunoinformatics approach

Chlamydiosis is a widespread ailment affecting humans, livestock, and wildlife, caused by C. abortus, a member of the Chlamydia genus. This disease leads to reproductive disorders in bovines and poses a zoonotic risk, resulting in adverse outcomes such as abortion, stillbirths, weak offspring, endometritis, repeat breeding, and perinatal mortality. However, current chlamydiosis vaccines have limitations in terms of safety, efficacy, and stability, necessitating the development of effective and safe alternatives. In this study, our objective was to design a multi-epitope vaccine (MEV) targeting all strains of C. abortus using bioinformatics and immunoinformatics approaches. We identified highly antigenic and non-allergic proteins (yidC, yajC, secY, CAB503, and CAB746) using VaxiJen and AlgPred tools. Physicochemical analyses and secondary structure predictions confirmed protein stability through ProtParam and SOPMA methods. Furthermore, we employed IEDB-AR, NETMHCpan, and ToxinPred2 tools to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, resulting in the identification of conserved epitopes for further analysis. The MEV construct, consisting of 545 amino acids, incorporated the adjuvant Beta defensin-3, along with 9 CTL epitopes and 21 HTL epitopes linked by EAAAK, KK, and AAY linkers. We assessed the safety and immunogenicity of the vaccine through comprehensive evaluations of antigenicity, toxicity, allergenicity, and physicochemical properties. Structural stability and quality were examined using 3D modeling via the ab initio approach with the Robetta platform. Molecular docking analysis explored the compatibility of the MEV with Toll-like receptor 9 (TLR9) using ClusPro, while molecular dynamics simulation with the DESMOND Maestro software predicted the stability and flexibility of the docked complex. Despite promising in silico findings, further wet lab investigations are crucial to validate the safety and efficacy of the MEV. Successful development and validation of this MEV hold significant potential in combatting chlamydiosis in both animal and human populations.Communicated by Ramaswamy H. Sarma.

Precision engineering of antibodies: A review of modification and design in the Fab region

The binding of functional groups to antibodies is crucial for disease treatment, diagnosis, and basic scientific research. Traditionally, antibody modifications have focused on the Fc region to maintain antigen-antibody binding activity. However, such modifications may impact critical antibody functions, including immune cell surface receptor activation, cytokine release, and other immune responses. In recent years, modifications targeting the antigen-binding fragment (Fab) region have garnered increasing attention. Precise modifications of the Fab region not only maximize the retention of antigen-antibody binding capacity but also enhance numerous physicochemical properties of antibodies. This paper reviews the chemical, biological, biochemical, and computer-assisted methods for modifying the Fab region of antibodies, discussing their advantages, limitations, recent advances, and future trends.

Humanization of the antigen-recognition domain does not impinge on the antigen-binding, cytokine secretion, and antitumor reactivity of humanized nanobody-based CD19-redirected CAR-T cells

BACKGROUND

The immunogenicity of the antigen-recognition domains of chimeric antigen receptor (CAR)-T cells leads to immune responses that may compromise the antitumor effects of the adoptively transferred T cells. Herein, we attempt to humanize a CD19-specific VHH (named H85) using in silico techniques and investigate the impact of antigen-recognition domain humanization on CAR expression and density, cytokine secretion, and cytolytic reactivity of CAR-T cells based on the humanized VHH.

METHODS

H85 was humanized (named HuH85), and then HuH85 was compared with H85 in terms of conformational structure, physicochemical properties, antigenicity and immunogenicity, solubility, flexibility, stability, and CD19-binding capacity using in silico techniques. Next, H85CAR-T cells and HuH85CAR-T cells were developed and CAR expression and surface density were assessed via flow cytometry. Ultimately, the antitumor reactivity and secreted levels of IFN-γ, IL-2, and TNF-α were assessed following the co-cultivation of the CAR-T cells with Ramos, Namalwa, and K562 cells.

RESULTS

In silico findings demonstrated no negative impacts on HuH85 as a result of humanization. Ultimately, H85CAR and HuH85CAR could be surface-expressed on transduced T cells at comparable levels as assessed via mean fluorescence intensity. Moreover, H85CAR-T cells and HuH85CAR-T cells mediated comparable antitumor effects against Ramos and Namalwa cells and secreted comparable levels of IFN-γ, IL-2, and TNF-α following co-cultivation.

CONCLUSION

HuH85 can be used to develop immunotherapeutics against CD19-associated hematologic malignancies. Moreover, HuH85CAR-T cells must be further investigated in vitro and in preclinical xenograft models of CD19+ leukemias and lymphomas before advancing into clinical trials.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

Bioengineered self-assembled nanofibrils for high-affinity SARS-CoV-2 capture and neutralization

The recent coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spurred intense research efforts to develop new materials with antiviral activity. In this study, we genetically engineered amyloid-based nanofibrils for capturing and neutralizing SARS-CoV-2. Building upon the amyloid properties of a short Sup35 yeast prion sequence, we fused it to SARS-CoV-2 receptor-binding domain (RBD) capturing proteins, LCB1 and LCB3. By tuning the reaction conditions, we achieved the spontaneous self-assembly of the Sup35-LCB1 fusion protein into a highly homogeneous and well-dispersed amyloid-like fibrillar material. These nanofibrils exhibited high affinity for the SARS-CoV-2 RBD, effectively inhibiting its interaction with the angiotensin-converting enzyme 2 (ACE2) receptor, the primary entry point for the virus into host cells. We further demonstrate that this functional nanomaterial entraps and neutralizes SARS-CoV-2 virus-like particles (VLPs), with a potency comparable to that of therapeutic antibodies. As a proof of concept, we successfully fabricated patterned surfaces that selectively capture SARS-CoV-2 RBD protein on wet environments. Collectively, these findings suggest that these protein-only nanofibrils hold promise as disinfecting coatings endowed with selective SARS-CoV-2 neutralizing properties to combat viral spread or in the development of sensitive viral sampling and diagnostic tools.

Chagasin from Trypanosoma cruzi as a molecular scaffold to express epitopes of TSA-1 as soluble recombinant chimeras

Trypanosoma cruzi is the causative agent of Chagas disease, a global public health problem. New therapeutic drugs and biologics are needed. The TSA-1 recombinant protein of T. cruzi is one such promising antigen for developing a therapeutic vaccine. However, it is overexpressed in E. coli as inclusion bodies, requiring an additional refolding step. As an alternative, in this study, we propose the endogenous cysteine protease inhibitor chagasin as a molecular scaffold to generate chimeric proteins. These proteins will contain combinations of two of the five conserved epitopes (E1 to E5) of TSA-1 in the L4 and L6 chagasin loops. Twenty chimeras (Q1-Q20) were designed, and their solubility was predicted using bioinformatics tools. Nine chimeras with different degrees of solubility were selected and expressed in E. coli BL21 (DE3). Western blot assays with anti-6x-His and anti-chagasin antibodies confirmed the expression of soluble recombinant chimeras. Both theoretically and experimentally, the Q12 (E5-E3) chimera was the most soluble, and the Q20 (E4-E5) the most insoluble protein. Q4 (E5-E1) and Q8 (E5-E2) chimeras were classified as proteins with medium solubility that exhibited the highest yield in the soluble fraction. Notably, Q4 has a yield of 239 mg/L, well above the yield of recombinant chagasin (16.5 mg/L) expressed in a soluble form. The expression of the Q4 chimera was scaled up to a 7 L fermenter obtaining a yield of 490 mg/L. These data show that chagasin can serve as a molecular scaffold for the expression of TSA-1 epitopes in the form of soluble chimeras.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Unraveling DEHP influence on hemoglobin S polymerization in sickle cell disease: Ex vivo, in vitro and in silico analysis

Sickle cell disease (SCD) is a hereditary hemoglobinopathy, caused by a mutation at position 6 of the β-globin chain and patients are frequently exposed to several blood transfusions in order to maintain physiological function. Transfusion blood bags are composed of PVC and phthalates (as DEHP) are often introduced to the material in order to confer malleability. In this sense, DEHP can easily elute to the blood and cause harmful effects. This study aimed to unravel DEHP effect on SCD patient's hemoglobin function. We found that HbS polymerization using whole erythrocytes is decreased by DEHP in ex vivo experiments and this effect might be mediated by the DEHP-VAL6 interaction, evaluated in silico. Isolated HbS exhibited less polymerization at low DEHP concentrations and increased polymerization rate at higher concentration. When analyzing the propensity to aggregate, HbS is more inclined to aggregate when compared to HbA due to the residue 6 mutation. Circular dichroism showed characteristic hemoglobin peaks for oxygenated HbS that are lost when oxygen is sequestered, and DEHP at higher concentration mildly recovers a peak close to the second hemoglobin one. Finally, by transmission electron microscopy we demonstrated that high DEHP concentration increased polymer formation with a more organized structure. These findings show for the first-time the beneficial effect of low-dose DEHP on HbS polymerization.

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Prediction of protein aggregation

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

QTY code designed antibodies for aggregation prevention: A structural bioinformatic and computational study

Therapeutic monoclonal antibodies are the most rapidly growing class of molecular medicine, and they are beneficial to the treatment of a broad spectrum of human diseases. However, the aggregation of antibodies during the process of manufacture, distribution, and storage poses significant challenges, potentially compromising efficacy and inducing adverse immune responses. We previously conceived a QTY (glutamine, threonine, tyrosine) code, a simple tool for enhancing protein water-solubility by systematically pairwise replacing hydrophobic residues L (leucine), V (valine)/I (isoleucine), and F (phenylalanine). The QTY code offers a promising alternative to traditional methods of controlling aggregation in integral transmembrane proteins. In this study, we designed variants of four antibodies applying the QTY code, changing only the β-sheets. Through the structure-based aggregation analysis, we found that these QTY antibody variants demonstrated significantly decreased aggregation propensity compared to their wild-type counter parts. Our results of molecular dynamics simulations showed that the design by QTY code is capable of maintaining the antigen-binding affinity and structural stability. Our structural informatic and computational study suggests that the QTY code offers a significant potential in mitigating antibody aggregation.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

PACT - Prediction of amyloid cross-interaction by threading

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Improving antibody optimization ability of generative adversarial network through large language model

Generative adversarial networks (GANs) have successfully generated functional protein sequences. However, traditional GANs often suffer from inherent randomness, resulting in a lower probability of obtaining desirable sequences. Due to the high cost of wet-lab experiments, the main goal of computer-aided antibody optimization is to identify high-quality candidate antibodies from a large range of possibilities, yet improving the ability of GANs to generate these desired antibodies is a challenge. In this study, we propose and evaluate a new GAN called the Language Model Guided Antibody Generative Adversarial Network (AbGAN-LMG). This GAN uses a language model as an input, harnessing such models' powerful representational capabilities to improve the GAN's generation of high-quality antibodies. We conducted a comprehensive evaluation of the antibody libraries and sequences generated by AbGAN-LMG for COVID-19 (SARS-CoV-2) and Middle East Respiratory Syndrome (MERS-CoV). Results indicate that AbGAN-LMG has learned the fundamental characteristics of antibodies and that it improved the diversity of the generated libraries. Additionally, when generating sequences using AZD-8895 as the target antibody for optimization, over 50% of the generated sequences exhibited better developability than AZD-8895 itself. Through molecular docking, we identified 70 antibodies that demonstrated higher affinity for the wild-type receptor-binding domain (RBD) of SARS-CoV-2 compared to AZD-8895. In conclusion, AbGAN-LMG demonstrates that language models used in conjunction with GANs can enable the generation of higher-quality libraries and candidate sequences, thereby improving the efficiency of antibody optimization. AbGAN-LMG is available at http://39.102.71.224:88/.

In silico molecular and functional characterization of a dual function antimicrobial peptide, hepcidin (GIFT-Hep), isolated from genetically improved farmed tilapia (GIFT, Oreochromis niloticus)

BACKGROUND

Antimicrobial peptides (AMPs), innate immune response molecules in organisms, are also known for their dual functionality, exemplified by hepcidin-an immunomodulator and iron regulator. Identifying and studying various AMPs from fish species can provide valuable insights into the immune profiles of aquaculturally significant fish, which can be made use of in its culture.

RESULTS

Hepcidin, a dual-function antimicrobial peptide, was isolated from the gill tissue of Genetically Improved Farmed Tilapia (GIFT-Hep). GIFT-Hep consists of a 90 amino acid pre-propeptide with a 24-mer signal, a 40-mer propeptide, and a 26-mer mature peptide region. The mature peptide had a molecular weight of 3015.61 Da, a theoretical pI of 8.78, a net charge of +4.25, and a protein-binding potential of 2.06 kcal/mol. Four disulfide bonds were formed by eight cysteine residues in the mature region. The presence of positively charged arginine residues renders the peptide 50% hydrophobic. Molecular analysis of GIFT-Hep revealed the presence of a furin propeptide convertase motif, RX(K/R)R, which facilitates trimming of the peptide to yield the mature GIFT-Hep. The hypothetical iron regulatory sequence, QSHLSL, was also identified in the mature peptide. In silico predictions about the characteristics of GIFT-Hep, such as charge, hydrophobicity, high surface accessibility, transmembrane helical regions, hydrophobic faces, hot spots, and cell-penetrating properties, suggest that the peptide functions as an iron regulatory antimicrobial agent.

CONCLUSIONS

This study reports a hepcidin antimicrobial peptide with both HAMP1 and HAMP2 properties isolated from genetically improved farmed tilapia, and further evaluation of the properties will prove the feasibility of GIFT-Hep being used as a therapeutant in aquaculture.

Prediction of Protein Aggregation Propensity via Data-Driven Approaches

Protein aggregation occurs when misfolded or unfolded proteins physically bind together and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in the Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed by using this database. The graph-based model outperformed the feature-based model, resulting in an R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using the Curated Protein Aggregation Database 2.0 to predict the aggregated intensity curves. In summary, this study suggests approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.

Reovirus genomic diversity confers plasticity for protease utility during adaptation to intracellular uncoating

IMPORTANCE

Reoviruses infect many mammals and are widely studied as a model system for enteric viruses. However, most of our reovirus knowledge comes from laboratory strains maintained on immortalized L929 cells. Herein, we asked whether naturally circulating reoviruses possess the same genetic and phenotypic characteristics as laboratory strains. Naturally circulating reoviruses obtained from sewage were extremely diverse genetically. Moreover, sewage reoviruses exhibited poor fitness on L929 cells and relied heavily on gut proteases for viral uncoating and productive infection compared to laboratory strains. We then examined how naturally circulating reoviruses might adapt to cell culture conditions. Within three passages, virus isolates from the parental sewage population were selected, displaying improved fitness and intracellular uncoating in L929 cells. Remarkably, selected progeny clones were present at 0.01% of the parental population. Altogether, using reovirus as a model, our study demonstrates how the high genetic diversity of naturally circulating viruses results in rapid adaptation to new environments.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Computer-aided design of proline-rich antimicrobial peptides based on the chemophysical properties of a peptide isolated from Olivancillaria hiatula

The chemophysical properties of a peptide isolated from Olivancillaria hiatula were combined with computational tools to design new antimicrobial peptides (AMPs). The in silico peptide design utilized arbitrary sequence shuffling, AMP sequence prediction and alignments such that putative sequences mimicked those of proline-rich AMPs (PrAMPs) and were potentially active against bacteria. Molecular modelling and docking experiments were used to monitor peptide binding to some intracellular targets like bacteria ribosome, DnaK and LasR. Peptide candidates were tested in vitro for antibacterial and antivirulence activities. Chemophysical studies of peptide extract suggested hydrophobic, acidic and proline-rich peptide properties. The amino acid signature of the extract matched that of AMPs that inhibit intracellular targets. Two of the designed PrAMP peptides (OhPrP-3 and OhPrP-5) had high affinity for the ribosome and DnaK. OhPrP-1, 2 and 4 also had favorable interactions with the biomolecular targets investigated. Peptides had bactericidal activity at the minimum inhibitory concentration against Pseudomonas aeruginosa. The designed peptides docked strongly to LasR suggesting possible interference with quorum sensing, and this was corroborated by in vitro data where sub-inhibitory doses of all peptides reduced pyocyanin and pyoverdine expression. The designed peptides can be further studied for the development of new anti-infective agents.

Prediction of protein aggregation propensity employing SqFt-based logistic regression model

Here we present a novel machine-learning approach to predict protein aggregation propensity (PAP) which is a key factor in the formation of amyloid fibrils based on logistic regression (LR). Amyloid fibrils are associated with various neurodegenerative diseases (ND) such as Alzheimer's disease (AD) and Parkinson's disease (PD), which are caused by oxidative stress and impaired protein homeostasis. Accordingly, the paper uses a dataset of hexapeptides with known aggregation tendencies and eight physiochemical features to train and test the LR model. Also, it evaluates the performance of the LR model using F-measure and Matthews correlation coefficient (MCC) as metrics and compares it with other existing methods. Moreover, it investigates the effect of combining sequence and feature information in the prediction. In conclusion, the LR model with sequence and feature information achieves high F-measure (0.841) and MCC (0.6692), outperforming other methods and demonstrating its efficiency and reliability for PAP prediction. In addition, the overall performance of the concluded method was higher than the other known servers, for instance, Aggrescan, Metamyl, Foldamyloid, and PASTA 2.0. The LR model can be accessed at: https://github.com/KatherineEshari/Protein-aggregation-prediction.

A3DyDB: exploring structural aggregation propensities in the yeast proteome

BACKGROUND

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation.

RESULTS

In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast .

CONCLUSION

The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

Antibody designing against IIIabc junction (JIIIabc) of HCV IRES through affinity maturation; RNA-Antibody docking and interaction analysis

Hepatitis C virus is a single-stranded RNA based virus which can cause chronic HCV and hepatocellular carcinoma. HCV genotype 3a has relatively higher rate of fibrosis progression, prevalence of steatosis and incidence of HCC. Despite HCVs variation in genomic sequence, the 5' untranslated region containing internal ribosome entry site (IRES) is highly conserved among all genotypes. It is responsible for translation and initiation of the viral protein. In present study, IRES was targeted by designing variants of reported antigen binding fragment (Fab) through affinity maturation approach. Affinity maturation strategy allowed the rational antibody designing with better biophysical properties and antibody-antigen binding interactions. Complementarity determining regions of reported Fab (wild type) were assessed and docked with IRES. Best generated model of Fab was selected and subjected to alanine scanning Three sets of insilico mutations for variants (V) designing were selected; single (1-71), double (a-j) and triple (I-X). Redocking of IRES-Fab variants consequently enabled the discovery of three variants exhibiting better docking score as compared to the wild type Fab. V1, V39 and V4 exhibited docking scores of -446.51, -446.52 and-446.29 kcal/mol respectively which is better as compared to the wild type Fab that exhibited the docking score of -351.23 kcal/mol. Variants exhibiting better docking score were screened for aggregation propensity by assessing the aggregation prone regions in Fab structure. Total A3D scores of wild type Fab, V1, V4 and V39 were predicted as -315.325, -312.727, -316.967 and -317.545 respectively. It is manifested that solubility of V4 and V39 is comparable to wild type Fab. In future, development and invitro assessment of these promising Fab HCV3 variants is aimed.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

Predictive Nature of High-Throughput Assays in ADC Formulation Screening

Utilization of high-throughput biophysical screening techniques during early screening studies is warranted due to the limited amount of material and large number of samples. But the predictability of the data to longer-term storage stability is critical as the high-throughput methods assist in defining the design space for the longer-term studies. In this study, the biophysical properties of two ADCs in 16 formulation conditions were evaluated using high-throughput techniques. Conformational stability and colloidal stability were evaluated by determining Tm values, kD, B22, and Tagg. In addition, the samples were placed on stability and the extent of aggregate formation over the 8-week interval was determined. The rank order of the 16 different formulations in the high-throughput assays was compared to the rank order observed during the stability studies to assess the predictive capabilities of the screening methods. It was demonstrated that similar rank orders can be expected between high-throughput physical stability indicating assays such as Tagg and B22 and traditional aggregation by SEC data, whereas conformational stability read-outs (Tm) are less predictive. In addition, the high-throughput assays appropriately identified the poor performing formulation conditions, which is ultimately what is desired of screening assays.

Combining Molecular Dynamics Simulations and Biophysical Characterization to Investigate Protein-Specific Excipient Effects on Reteplase during Freeze Drying

We performed molecular dynamics simulations of Reteplase in the presence of different excipients to study the stabilizing mechanisms and to identify the role of excipients during freeze drying. To simulate the freeze-drying process, we divided the process into five distinct steps: (i) protein-excipient formulations at room temperature, (ii) the ice-growth process, (iii)-(iv) the partially solvated and fully dried formulations, and (v) the reconstitution. Furthermore, coarse-grained (CG) simulations were employed to explore the protein-aggregation process in the presence of arginine. By using a coarse-grained representation, we could observe the collective behavior and interactions between protein molecules during the aggregation process. The CG simulations revealed that the presence of arginine prevented intermolecular interactions of the catalytic domain of Reteplase, thus reducing the aggregation propensity. This suggests that arginine played a stabilizing role by interacting with protein-specific regions. From the freeze-drying simulations, we could identify several protein-specific events: (i) collapse of the domain structure, (ii) recovery of the drying-induced damages during reconstitution, and (iii) stabilization of the local aggregation-prone region via direct interactions with excipients. Complementary to the simulations, we employed nanoDSF, size-exclusion chromatography, and CD spectroscopy to investigate the effect of the freeze-drying process on the protein structure and stability.