NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

Size-tunable transmembrane nanopores assembled from decomposable molecular templates

Transmembrane nanopores, as key elements in molecular transport and single-molecule sensors, are assembled naturally from multiple monomers in the presence of lipid bilayers. The nanopore size, especially the precise diameter of the inner space, determines its sensing targets and further biological application. In this paper, we introduce a template molecule-aided assembly strategy for constructing size-tunable transmembrane nanopores. Inspired by the barrel-like structure, similar to many transmembrane proteins, cyclodextrin molecules of different sizes are utilized as templates and modulators to assemble the α-helical barreled peptide of polysaccharide transporters (Wza). The functional nanopores assembled by this strategy possess high biological and chemical activity and can be inserted into lipid bilayers, forming stable single channels for single-molecule sensing. After enzyme digestion, the cyclodextrins on protein nanopores can be degraded, and the remaining nontemplate transmembrane protein nanopores can also preserve the integrity of their structure and function. The template molecule-aided assembly strategy employed a simple and convenient method for fully artificially synthesizing transmembrane protein nanopores; the pore size is completely dependent on the size of the template molecule and controllable, ranging from 1.1 to 1.8 nm. Furthermore, by chemically synthesized peptides and modifications, the pore function is easily modulated and does not involve the cumbersome genetic mutations of other biological techniques.

Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers' pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification

The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .

Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects

Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.

Sex-specific gene expression differences in the prefrontal cortex of major depressive disorder individuals

Major depressive disorder (MDD) is a leading global cause of disability, being more prevalent in females, possibly due to molecular and neuronal pathway differences between females and males. However, the connection between transcriptional changes and MDD remains unclear. We identified transcriptionally altered genes (TAGs) in MDD through gene and transcript expression analyses, focusing on sex-specific differences. Analyzing 263 brain samples from both sexes, we conducted differential gene expression, differential transcript expression, and differential transcript usage analyses, revealing 1169 unique TAGs, primarily in the prefrontal areas, with nearly half exhibiting transcript-level alterations. Females showed notable RNA splicing and export process disruptions in the orbitofrontal cortex, alongside altered DDX39B gene expression in five of the six brain regions in both sexes. Our findings suggest that disruptions in RNA processing pathways may play a vital role in MDD.

Novel dual-pathogen multi-epitope mRNA vaccine development for Brucella melitensis and Mycobacterium tuberculosis in silico approach

Brucellosis and Tuberculosis, both of which are contagious diseases, have presented significant challenges to global public health security in recent years. Delayed treatment can exacerbate the conditions, jeopardizing patient lives. Currently, no vaccine has been approved to prevent these two diseases simultaneously. In contrast to traditional vaccines, mRNA vaccines offer advantages such as high efficacy, rapid development, and low cost, and their applications are gradually expanding. This study aims to develop multi-epitope mRNA vaccines argeting Brucella melitensis and Mycobacterium tuberculosis H37Rv (L4 strain) utilizing immunoinformatics approaches. The proteins Omp25, Omp31, MPT70, and MPT83 from the specified bacteria were selected to identify the predominant T- and B-cell epitopes for immunological analysis. Following a comprehensive evaluation, a vaccine was developed using helper T lymphocyte epitopes, cytotoxic T lymphocyte epitopes, linear B-cell epitopes, and conformational B-cell epitopes. It has been demonstrated that multi-epitope mRNA vaccines exhibit increased antigenicity, non-allergenicity, solubility, and high stability. The findings from molecular docking and molecular dynamics simulation revealed a robust and enduring binding affinity between multi-epitope peptides mRNA vaccines and TLR4. Ultimately, Subsequently, following the optimization of the nucleotide sequence, the codon adaptation index was calculated to be 1.0, along with an average GC content of 54.01%. This indicates that the multi-epitope mRNA vaccines exhibit potential for efficient expression within the Escherichia coli(E. coli) host. Analysis through immune modeling indicates that following administration of the vaccine, there may be variation in immunecell populations associated with both innate and adaptive immune reactions. These types encompass helper T lymphocytes (HTL), cytotoxic T lymphocytes (CTL), regulatory T lymphocytes, natural killer cells, dendritic cells and various immune cell subsets. In summary, the results suggest that the newly created multi-epitope mRNA vaccine exhibits favorable attributes, offering novel insights and a conceptual foundation for potential progress in vaccine development.

Adaptive strategies of Listeria monocytogenes: An in-depth analysis of the virulent strain involved in an outbreak in Italy through quantitative proteomics

Despite the general classification of L. monocytogenes strains as equally virulent by global safety authorities, molecular epidemiology reveals diverse subtypes in food, processing environments, and clinical cases. This study focuses on a highly virulent strain associated with a listeriosis outbreak in Italy in 2022, providing insights through comprehensive foodomics approaches, with a specific emphasis on quantitative proteomics. In particular, the ST155 strain of L. monocytogenes strain was subjected in vitro to growth stress conditions (NaCl 2.4 %, pH 6.2, T 12 °C), mimicking the conditions present in the frankfurter, its original source. Then, the protein expression patterns were compared with those obtained in optimal growth conditions. Through quantitative proteomic analysis and bioinformatic assessment, different proteins associated with virulence during the exponential growth phase were identified. This study unveils unique proteins specific to each environment, providing insights into how L. monocytogenes adapts to conditions that are similar to those encountered in frankfurters. This investigation contributes valuable insights into the adaptive strategies of L. monocytogenes under stressful conditions, with implications for enhancing food safety practices.

ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure

BACKGROUND

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research.

OBJECTIVES

We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage.

METHODS

To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures.

RESULTS

Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches.

CONCLUSIONS

This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.

DCMA: faster protein backbone dihedral angle prediction using a dilated convolutional attention-based neural network

The dihedral angle of the protein backbone can describe the main structure of the protein, which is of great significance for determining the protein structure. Many computational methods have been proposed to predict this critically important protein structure, including deep learning. However, these heavyweight methods require more computational resources, and the training time becomes intolerable. In this article, we introduce a novel lightweight method, named dilated convolution and multi-head attention (DCMA), that predicts protein backbone torsion dihedral angles ( ϕ , ψ ) . DCMA is stacked by five layers of two hybrid inception blocks and one multi-head attention block (I2A1) module. The hybrid inception blocks consisting of multi-scale convolutional neural networks and dilated convolutional neural networks are designed for capturing local and long-range sequence-based features. The multi-head attention block supplementally strengthens this operation. The proposed DCMA is validated on public critical assessment of protein structure prediction (CASP) benchmark datasets. Experimental results show that DCMA obtains better or comparable generalization performance. Compared to best-so-far methods, which are mostly ensemble models and constructed of recurrent neural networks, DCMA is an individual model that is more lightweight and has a shorter training time. The proposed model could be applied as an alternative method for predicting other protein structural features.

PatchProt: hydrophobic patch prediction using protein foundation models

Motivation

Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods.

Results

In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks.

Availability and implementation

https://github.com/Deagogishvili/chapter-multi-task.

Surface protein distribution in Group B Streptococcus isolates from South Africa and identifying vaccine targets through in silico analysis

Group B Streptococcus (GBS) is a major cause of pneumonia, sepsis, and meningitis in infants younger than 3 months of age. Furthermore, GBS infection in pregnant women is associated with stillbirths and pre-term delivery. It also causes disease in immunocompromised adults and the elderly, but the highest incidence of the disease occurs in neonates and young infants. At this time, there are no licensed vaccines against GBS. Complete GBS genome sequencing has helped identify genetically conserved and immunogenic proteins, which could serve as vaccine immunogens. In this study, in silico reverse vaccinology method were used to evaluate the prevalence and conservation of GBS proteins in invasive and colonizing isolates from South African infants and women, respectively. Furthermore, this study aimed to predict potential GBS vaccine targets by evaluating metrics such as antigenicity, physico-chemical properties, subcellular localization, secondary and tertiary structures, and epitope prediction and conservation. A total of 648 invasive and 603 colonizing GBS isolate sequences were screened against a panel of 89 candidate GBS proteins. Ten of the 89 proteins were highly genetically conserved in invasive and colonizing GBS isolates, nine of which were computationally inferred proteins (gbs2106, SAN_1577, SAN_0356, SAN_1808, SAN_1685, SAN_0413, SAN_0990, SAN_1040, SAN_0226) and one was the surface Immunogenic Protein (SIP). Additionally, the nine proteins were predicted to be more antigenic than the SIP protein (antigenicity score of > 0.6498), highlighting their potential as GBS vaccine antigen targets.

PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy

Proteins perform different biological functions through binding with various molecules which are mediated by a few key residues and accurate prediction of such protein binding residues (PBRs) is crucial for understanding cellular processes and for designing new drugs. Many computational prediction approaches have been proposed to identify PBRs with sequence-based features. However, these approaches face two main challenges: (1) these methods only concatenate residue feature vectors with a simple sliding window strategy, and (2) it is challenging to find a uniform sliding window size suitable for learning embeddings across different types of PBRs. In this study, we propose one novel framework that could apply multiple types of PBRs Prediciton task through Multi-scale Sequence-based Feature Fusion (PMSFF) strategy. Firstly, PMSFF employs a pre-trained language model named ProtT5, to encode amino acid residues in protein sequences. Then, it generates multi-scale residue embeddings by applying multi-size windows to capture effective neighboring residues and multi-size kernels to learn information across different scales. Additionally, the proposed model treats protein sequences as sentences, employing a bidirectional GRU to learn global context. We also collect benchmark datasets encompassing various PBRs types and evaluate our PMSFF approach to these datasets. Compared with state-of-the-art methods, PMSFF demonstrates superior performance on most PBRs prediction tasks.

Progress and opportunities of foundation models in bioinformatics

Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly through foundation models (FMs), which address longstanding challenges in bioinformatics such as limited annotated data and data noise. These AI techniques have demonstrated remarkable efficacy across various downstream validation tasks, effectively representing diverse biological entities and heralding a new era in computational biology. The primary goal of this survey is to conduct a general investigation and summary of FMs in bioinformatics, tracing their evolutionary trajectory, current research landscape, and methodological frameworks. Our primary focus is on elucidating the application of FMs to specific biological problems, offering insights to guide the research community in choosing appropriate FMs for tasks like sequence analysis, structure prediction, and function annotation. Each section delves into the intricacies of the targeted challenges, contrasting the architectures and advancements of FMs with conventional methods and showcasing their utility across different biological domains. Further, this review scrutinizes the hurdles and constraints encountered by FMs in biology, including issues of data noise, model interpretability, and potential biases. This analysis provides a theoretical groundwork for understanding the circumstances under which certain FMs may exhibit suboptimal performance. Lastly, we outline prospective pathways and methodologies for the future development of FMs in biological research, facilitating ongoing innovation in the field. This comprehensive examination not only serves as an academic reference but also as a roadmap for forthcoming explorations and applications of FMs in biology.

The GFAP proteoform puzzle: How to advance GFAP as a fluid biomarker in neurological diseases

Glial fibrillary acidic protein (GFAP) is a well-established biomarker of reactive astrogliosis in the central nervous system because of its elevated levels following brain injury and various neurological disorders. The advent of ultra-sensitive methods for measuring low-abundant proteins has significantly enhanced our understanding of GFAP levels in the serum or plasma of patients with diverse neurological diseases. Clinical studies have demonstrated that GFAP holds promise both as a diagnostic and prognostic biomarker, including but not limited to individuals with Alzheimer's disease. GFAP exhibits diverse forms and structures, herein referred to as its proteoform complexity, encompassing conformational dynamics, isoforms and post-translational modifications (PTMs). In this review, we explore how the proteoform complexity of GFAP influences its detection, which may affect the differential diagnostic performance of GFAP in different biological fluids and can provide valuable insights into underlying biological processes. Additionally, proteoforms are often disease-specific, and our review provides suggestions and highlights areas to focus on for the development of new assays for measuring GFAP, including isoforms, PTMs, discharge mechanisms, breakdown products, higher-order species and interacting partners. By addressing the knowledge gaps highlighted in this review, we aim to support the clinical translation and interpretation of GFAP in both CSF and blood and the development of reliable, reproducible and specific prognostic and diagnostic tests. To enhance disease pathology comprehension and optimise GFAP as a biomarker, a thorough understanding of detected proteoforms in biofluids is essential.

A comprehensive in silico investigation into the pathogenic SNPs in the RTEL1 gene and their biological consequences

The Regulator of Telomere Helicase 1 (RTEL1) gene encodes a critical DNA helicase intricately involved in the maintenance of telomeric structures and the preservation of genomic stability. Germline mutations in the RTEL1 gene have been clinically associated with Hoyeraal-Hreidarsson syndrome, a more severe version of Dyskeratosis Congenita. Although various research has sought to link RTEL1 mutations to specific disorders, no comprehensive investigation has yet been conducted on missense mutations. In this study, we attempted to investigate the functionally and structurally deleterious coding and non-coding SNPs of the RTEL1 gene using an in silico approach. Initially, out of 1392 nsSNPs, 43 nsSNPs were filtered out through ten web-based bioinformatics tools. With subsequent analysis using nine in silico tools, these 43 nsSNPs were further shortened to 11 most deleterious nsSNPs. Furthermore, analyses of mutated protein structures, evolutionary conservancy, surface accessibility, domains & PTM sites, cancer susceptibility, and interatomic interaction revealed the detrimental effect of these 11 nsSNPs on RTEL1 protein. An in-depth investigation through molecular docking with the DNA binding sequence demonstrated a striking change in the interaction pattern for F15L, M25V, and G706R mutant proteins, suggesting the more severe consequences of these mutations on protein structure and functionality. Among the non-coding variants, two had the highest likelihood of being regulatory variants, whereas one variant was predicted to affect the target region of a miRNA. Thus, this study lays the groundwork for extensive analysis of RTEL1 gene variants in the future, along with the advancement of precision medicine and other treatment modalities.

Assessing the role of evolutionary information for enhancing protein language model embeddings

Embeddings from protein Language Models (pLMs) are replacing evolutionary information from multiple sequence alignments (MSAs) as the most successful input for protein prediction. Is this because embeddings capture evolutionary information? We tested various approaches to explicitly incorporate evolutionary information into embeddings on various protein prediction tasks. While older pLMs (SeqVec, ProtBert) significantly improved through MSAs, the more recent pLM ProtT5 did not benefit. For most tasks, pLM-based outperformed MSA-based methods, and the combination of both even decreased performance for some (intrinsic disorder). We highlight the effectiveness of pLM-based methods and find limited benefits from integrating MSAs.

Artificial Intelligence Learns Protein Prediction

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.

Are genomic language models all you need? Exploring genomic language models on protein downstream tasks

MOTIVATION

Large language models, trained on enormous corpora of biological sequences, are state-of-the-art for downstream genomic and proteomic tasks. Since the genome contains the information to encode all proteins, genomic language models (gLMs) hold the potential to make downstream predictions not only about DNA sequences, but also about proteins. However, the performance of gLMs on protein tasks remains unknown, due to few tasks pairing proteins with the coding DNA sequences (CDS) that can be processed by gLMs.

RESULTS

In this work, we curated five such datasets and used them to evaluate the performance of gLMs and proteomic language models (pLMs). We show that gLMs are competitive and even outperform their pLMs counterparts on some tasks. The best performance was achieved using the retrieved CDS compared to sampling strategies. We found that training a joint genomic-proteomic model outperforms each individual approach, showing that they capture different but complementary sequence representations, as we demonstrate through model interpretation of their embeddings. Lastly, we explored different genomic tokenization schemes to improve downstream protein performance. We trained a new Nucleotide Transformer (50M) foundation model with 3mer tokenization that outperforms its 6mer counterpart on protein tasks while maintaining performance on genomics tasks. The application of gLMs to proteomics offers the potential to leverage rich CDS data, and in the spirit of the central dogma, the possibility of a unified and synergistic approach to genomics and proteomics.

AVAILABILITY AND IMPLEMENTATION

We make our inference code, 3mer pre-trained model weights and datasets available.

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

TgVax452, an epitope-based candidate vaccine targeting Toxoplasma gondii tachyzoite-specific SAG1-related sequence (SRS) proteins: immunoinformatics, structural simulations and experimental evidence-based approaches

BACKGROUND

The highly expressed surface antigen 1 (SAG1)-related sequence (SRS) proteins of T. gondii tachyzoites, as a widespread zoonotic parasite, are critical for host cell invasion and represent promising vaccine targets. In this study, we employed a computer-aided multi-method approach for in silico design and evaluation of TgVax452, an epitope-based candidate vaccine against T. gondii tachyzoite-specific SRS proteins.

METHODS

Using immunoinformatics web-based tools, structural modeling, and static/dynamic molecular simulations, we identified and screened B- and T-cell immunodominant epitopes and predicted TgVax452's antigenicity, stability, safety, adjuvanticity, and physico-chemical properties.

RESULTS

The designed protein possessed 452 residues, a MW of 44.07 kDa, an alkaline pI (6.7), good stability (33.20), solubility (0.498), and antigenicity (0.9639) with no allergenicity. Comprehensive molecular dynamic (MD) simulation analyses confirmed the stable interaction (average potential energy: 3.3799 × 106 KJ/mol) between the TLR4 agonist residues (RS09 peptide) of the TgVax452 in interaction with human TLR4, potentially activating innate immune responses. Also, a dramatic increase was observed in specific antibodies (IgM and IgG), cytokines (IFN-γ), and lymphocyte responses, based on C-ImmSim outputs. Finally, we optimized TgVax452's codon adaptation and mRNA secondary structure for efficient expression in E. coli BL21 expression machinery.

CONCLUSION

Our findings suggest that TgVax452 is a promising candidate vaccine against T. gondii tachyzoite-specific SRS proteins and requires further experimental studies for its potential use in preclinical trials.

Fine-tuning protein language models boosts predictions across diverse tasks

Prediction methods inputting embeddings from protein language models have reached or even surpassed state-of-the-art performance on many protein prediction tasks. In natural language processing fine-tuning large language models has become the de facto standard. In contrast, most protein language model-based protein predictions do not back-propagate to the language model. Here, we compare the fine-tuning of three state-of-the-art models (ESM2, ProtT5, Ankh) on eight different tasks. Two results stand out. Firstly, task-specific supervised fine-tuning almost always improves downstream predictions. Secondly, parameter-efficient fine-tuning can reach similar improvements consuming substantially fewer resources at up to 4.5-fold acceleration of training over fine-tuning full models. Our results suggest to always try fine-tuning, in particular for problems with small datasets, such as for fitness landscape predictions of a single protein. For ease of adaptability, we provide easy-to-use notebooks to fine-tune all models used during this work for per-protein (pooling) and per-residue prediction tasks.

AI-based Prediction of Protein Corona Composition on DNA Nanostructures

DNA nanotechnology has emerged as a powerful approach to engineering biophysical tools, therapeutics, and diagnostics because it enables the construction of designer nanoscale structures with high programmability. Based on DNA base pairing rules, nanostructure size, shape, surface functionality, and structural reconfiguration can be programmed with a degree of spatial, temporal, and energetic precision that is difficult to achieve with other methods. However, the properties and structure of DNA constructs are greatly altered in vivo due to spontaneous protein adsorption from biofluids. These adsorbed proteins, referred to as the protein corona, remain challenging to control or predict, and subsequently, their functionality and fate in vivo are difficult to engineer. To address these challenges, we prepared a library of diverse DNA nanostructures and investigated the relationship between their design features and the composition of their protein corona. We identified protein characteristics important for their adsorption to DNA nanostructures and developed a machine-learning model that predicts which proteins will be enriched on a DNA nanostructure based on the DNA structures' design features and protein properties. Our work will help to understand and program the function of DNA nanostructures in vivo for biophysical and biomedical applications.

Centrosomal Protein 55 Regulates Chromosomal Instability in Cancer Cells by Controlling Microtubule Dynamics

Centrosomal Protein 55 (CEP55) exhibits various oncogenic activities; it regulates the PI3K-Akt-pathway, midbody abscission, and chromosomal instability (CIN) in cancer cells. Here, we analyzed the mechanism of how CEP55 controls CIN in ovarian and breast cancer (OvCa) cells. Down-regulation of CEP55 reduced CIN in all cell lines analyzed, and CEP55 depletion decreased spindle microtubule (MT)-stability in OvCa cells. Moreover, recombinant CEP55 accelerated MT-polymerization and attenuated cold-induced MT-depolymerization. To analyze a potential relationship between CEP55-controlled CIN and its impact on MT-stability, we identified the CEP55 MT-binding peptides inside the CEP55 protein. Thereafter, a mutant with deficient MT-binding activity was re-expressed in CEP55-depleted OvCa cells and we could show that this mutant did not restore reduced CIN in CEP55-depleted cells. This finding strongly indicates that CEP55 regulates CIN by controlling MT dynamics.

Disorder-to-order active site capping regulates the rate-limiting step of the inositol pathway

Myo-inositol-1-phosphate synthase (MIPS) catalyzes the NAD+-dependent isomerization of glucose-6-phosphate (G6P) into inositol-1-phosphate (IMP), controlling the rate-limiting step of the inositol pathway. Previous structural studies focused on the detailed molecular mechanism, neglecting large-scale conformational changes that drive the function of this 240 kDa homotetrameric complex. In this study, we identified the active, endogenous MIPS in cell extracts from the thermophilic fungus Thermochaetoides thermophila. By resolving the native structure at 2.48 Å (FSC = 0.143), we revealed a fully populated active site. Utilizing 3D variability analysis, we uncovered conformational states of MIPS, enabling us to directly visualize an order-to-disorder transition at its catalytic center. An acyclic intermediate of G6P occupied the active site in two out of the three conformational states, indicating a catalytic mechanism where electrostatic stabilization of high-energy intermediates plays a crucial role. Examination of all isomerases with known structures revealed similar fluctuations in secondary structure within their active sites. Based on these findings, we established a conformational selection model that governs substrate binding and eventually inositol availability. In particular, the ground state of MIPS demonstrates structural configurations regardless of substrate binding, a pattern observed across various isomerases. These findings contribute to the understanding of MIPS structure-based function, serving as a template for future studies targeting regulation and potential therapeutic applications.

The Entamoeba histolytica Vps26 (EhVps26) retromeric protein is involved in phagocytosis: Bioinformatic and experimental approaches

The retromer is a cellular structure that recruits and recycles proteins inside the cell. In mammalian and yeast, the retromer components have been widely studied, but very little in parasites. In yeast, it is formed by a SNX-BAR membrane remodeling heterodimer and the cargo selecting complex (CSC), composed by three proteins. One of them, the Vps26 protein, possesses a flexible and intrinsically disordered region (IDR), that facilitates interactions with other proteins and contributes to the retromer binding to the endosomal membrane. In Entamoeba histolytica, the protozoan parasite responsible for human amoebiasis, the retromer actively participates during the high mobility and phagocytosis of trophozoites, but the molecular details in these events, are almost unknown. Here, we studied the EhVps26 role in phagocytosis. Bioinformatic analyses of EhVps26 revealed a typical arrestin folding structure of the protein, and a long and charged IDR, as described in other systems. EhVps26 molecular dynamics simulations (MDS) allowed us to predict binding pockets for EhVps35, EhSNX3, and a PX domain-containing protein; these pockets were disorganized in a EhVps26 truncated version lacking the IDR. The AlphaFold2 software predicted the interaction of EhVps26 with EhVps35, EhVps29 and EhSNX3, in a model similar to the reported mammalian crystals. By confocal and transmission electron microscopy, EhVps26 was found in the trophozoites plasma membrane, cytosol, endosomes, and Golgi-like apparatus. During phagocytosis, it followed the erythrocytes pathway, probably participating in cargoes selection and recycling. Ehvps26 gene knocking down evidenced that the EhVps26 protein is necessary for efficient phagocytosis.

Simple prerequisite of presequence for mitochondrial protein import in the unicellular red alga Cyanidioschyzon merolae

Mitochondrial biogenesis relies on hundreds of proteins that are derived from genes encoded in the nucleus. According to the characteristic properties of N-terminal targeting peptides (TPs) and multi-step authentication by the protein translocase called the TOM complex, nascent polypeptides satisfying the requirements are imported into mitochondria. However, it is unknown whether eukaryotic cells with a single mitochondrion per cell have a similar complexity of presequence requirements for mitochondrial protein import compared to other eukaryotes with multiple mitochondria. Based on putative mitochondrial TP sequences in the unicellular red alga Cyanidioschyzon merolae, we designed synthetic TPs and showed that functional TPs must have at least one basic residue and a specific amino acid composition, although their physicochemical properties are not strictly determined. Combined with the simple composition of the TOM complex in C. merolae, our results suggest that a regional positive charge in TPs is verified solely by TOM22 for mitochondrial protein import in C. merolae. The simple authentication mechanism indicates that the monomitochondrial C. merolae does not need to increase the cryptographic complexity of the lock-and-key mechanism for mitochondrial protein import.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

The rate of adaptive molecular evolution in wild and domesticated Saccharomyces cerevisiae populations

Through its fermentative capacities, Saccharomyces cerevisiae was central in the development of civilisation during the Neolithic period, and the yeast remains of importance in industry and biotechnology, giving rise to bona fide domesticated populations. Here, we conduct a population genomic study of domesticated and wild populations of S. cerevisiae. Using coalescent analyses, we report that the effective population size of yeast populations decreased since the divergence with S. paradoxus. We fitted models of distributions of fitness effects to infer the rate of adaptive (ω a ) and non-adaptive (ω na ) non-synonymous substitutions in protein-coding genes. We report an overall limited contribution of positive selection to S. cerevisiae protein evolution, albeit with higher rates of adaptive evolution in wild compared to domesticated populations. Our analyses revealed the signature of background selection and possibly Hill-Robertson interference, as recombination was found to be negatively correlated withω na and positively correlated withω a . However, the effect of recombination onω a was found to be labile, as it is only apparent after removing the impact of codon usage bias on the synonymous site frequency spectrum and disappears if we control for the correlation withω na , suggesting that it could be an artefact of the decreasing population size. Furthermore, the rate of adaptive non-synonymous substitutions is significantly correlated with the residue solvent exposure, a relation that cannot be explained by the population's demography. Together, our results provide a detailed characterisation of adaptive mutations in protein-coding genes across S. cerevisiae populations.

Caffeine improves mitochondrial dysfunction in the white matter of neonatal rats with hypoxia-ischemia through deacetylation: a proteomic analysis of lysine acetylation

Aims

White matter damage (WMD) is linked to both cerebral palsy and cognitive deficits in infants born prematurely. The focus of this study was to examine how caffeine influences the acetylation of proteins within the neonatal white matter and to evaluate its effectiveness in treating white matter damage caused by hypoxia-ischemia.

Main methods

We employed a method combining affinity enrichment with advanced liquid chromatography and mass spectrometry to profile acetylation in proteins from the white matter of neonatal rats grouped into control (Sham), hypoxic-ischemic (HI), and caffeine-treated (Caffeine) groups.

Key findings

Our findings included 1,999 sites of lysine acetylation across 1,123 proteins, with quantifiable changes noted in 1,342 sites within 689 proteins. Analysis of these patterns identified recurring sequences adjacent to the acetylation sites, notably YKacN, FkacN, and G * * * GkacS. Investigation into the biological roles of these proteins through Gene Ontology analysis indicated their involvement in a variety of cellular processes, predominantly within mitochondrial locations. Further analysis indicated that the acetylation of tau (Mapt), a protein associated with microtubules, was elevated in the HI condition; however, caffeine treatment appeared to mitigate this over-modification, thus potentially aiding in reducing oxidative stress, inflammation in the nervous system, and improving mitochondrial health. Caffeine inhibited acetylated Mapt through sirtuin 2 (SITR2), promoted Mapt nuclear translocation, and improved mitochondrial dysfunction, which was subsequently weakened by the SIRT2 inhibitor, AK-7.

Significance

Caffeine-induced changes in lysine acetylation may play a key role in improving mitochondrial dysfunction and inhibiting oxidative stress and neuroinflammation.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms

BACKGROUND

Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them.

METHODS

The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains.

RESULTS

Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance.

CONCLUSIONS

The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks.

GENERAL SIGNIFICANCE

The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in Protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%.Communicated by Ramaswamy H. Sarma.

Evaluate the potential use of TonB-dependent receptor protein as a subunit vaccine against Aeromonas veronii infection in Nile tilapia (Oreochromis niloticus)

Aeromonas veronii is an emerging bacterial pathogen that causes serious systemic infections in cultured Nile tilapia (Oreochromis niloticus), leading to massive deaths. Therefore, there is an urgent need to identify effective vaccine candidates to control the spread of this emerging disease. TonB-dependent receptor (Tdr) of A. veronii, which plays a role in the virulence factor of the organism, could be useful in terms of protective antigens for vaccine development. This study aims to evaluate the potential use of Tdr protein as a novel subunit vaccine against A. veronii infection in Nile tilapia. The Tdr gene from A. veronii was cloned into the pET28b expression vector, and the recombinant protein was subsequently produced in Escherichia coli strain BL21 (DE3). Tdr was expressed as an insoluble protein and purified by affinity chromatography. Antigenicity test indicated that this protein was recognized by serum from A. veronii infected fish. When Nile tilapia were immunized with the Tdr protein, specific antibody levels increased significantly (p-value <0.05) at 7 days post-immunization (dpi), and peaked at 21 dpi compared to antibody levels at 0 dpi. Furthermore, bacterial agglutination activity was observed in the fish serum immunized with the Tdr protein, indicating that specific antibodies in the serum can detect Tdr on the bacterial cell surface. These results suggest that Tdr protein has potential as a vaccine candidate. However, challenging tests with A.veronii in Nile tilapia needs to be investigated to thoroughly evaluate its protective efficacy for future applications.

Evaluating generalizability of artificial intelligence models for molecular datasets

Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.

Pathological mutations reveal the key role of the cytosolic iRhom2 N-terminus for phosphorylation-independent 14-3-3 interaction and ADAM17 binding, stability, and activity

The protease ADAM17 plays an important role in inflammation and cancer and is regulated by iRhom2. Mutations in the cytosolic N-terminus of human iRhom2 cause tylosis with oesophageal cancer (TOC). In mice, partial deletion of the N-terminus results in a curly hair phenotype (cub). These pathological consequences are consistent with our findings that iRhom2 is highly expressed in keratinocytes and in oesophageal cancer. Cub and TOC are associated with hyperactivation of ADAM17-dependent EGFR signalling. However, the underlying molecular mechanisms are not understood. We have identified a non-canonical, phosphorylation-independent 14-3-3 interaction site that encompasses all known TOC mutations. Disruption of this site dysregulates ADAM17 activity. The larger cub deletion also includes the TOC site and thus also dysregulated ADAM17 activity. The cub deletion, but not the TOC mutation, also causes severe reductions in stimulated shedding, binding, and stability of ADAM17, demonstrating the presence of additional regulatory sites in the N-terminus of iRhom2. Overall, this study contrasts the TOC and cub mutations, illustrates their different molecular consequences, and reveals important key functions of the iRhom2 N-terminus in regulating ADAM17.

ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies

Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticancer peptides (ACPs) have emerged as promising candidates for highly specific anticancer drugs, and a number of computational approaches have been developed to identify ACPs. However, existing methods do not recognize specific types of anticancer function. In this article, we propose ACPScanner, an integrated approach to predict ACPs and non-ACPs at first and then predict several specific activity types for potential ACPs. We incorporate sequential, physicochemical properties, secondary structural information, and deep representation learning embeddings which are generated from artificial intelligence methods to build feature space. Customized deep learning and statistical learning methods are combined to form an integral architecture for the comprehensive two-level prediction task. To the best of our knowledge, ACPScanner is the first approach for specific ACP activity prediction. The comparative evaluation illustrates that ACPScanner achieves competitive prediction performance in both prediction phases in independent testings. We establish a web server at http://acpscanner.denglab.org to provide convenient usage of ACPScanner and make the predictive framework, source code, and data sets publicly available.

DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy

Revealing the molecular effect that pathogenic missense mutations have on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast targeted drug development at a low cost. Here, we report an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG, but do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp, and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

Structure-based evaluation of the envelope domain III-nonstructural protein 1 (EDIII-NS1) fusion as a dengue virus vaccine candidate

The lack of effective medicines or vaccines, combined with climate change and other environmental factors, annually subjects a significant proportion of the world's inhabitants to the risk of dengue virus (DENV) infection. These conditions increase the likelihood of exposure to mosquito-borne diseases such as dengue fever. Hence, many research approaches tend to develop efficient vaccine candidates against the dengue virus. Therefore, we used immunoinformatics and bioinformatics to design a construction for developing a candidate vaccine against dengue virus serotypes. In this study, the in silico structure, containing the non-structural protein 1 region (NS1) (consensus and epitope), the envelope domain III protein (EDIII) as the structural part of the virus construction, and the bc-loop of envelope domain II (EDII) as the neutralizing and protected epitope, were employed. We utilized in silico tools to enhance the immunogenicity and effectiveness of dengue virus vaccine candidates. Evaluations included refining and validating physicochemical characteristics, B and T-cell epitopes, homology modeling, and the three-dimensional structure to assess the designed vaccine's quality. In silico results for tertiary structure prediction and validation revealed high-quality modeling for all vaccine constructs. Additionally, the instructed model demonstrated stability throughout molecular dynamics simulation. The results of the immune simulation suggested that the titers of IgG and IgM could be raised to desirable values following injection into in vivo models. It can be concluded that the designed construct effectively induce humoral and cellular immunity and can be proposed as effective vaccine candidate against four dengue serotypes.Communicated by Ramaswamy H. Sarma.

PS4: a next-generation dataset for protein single-sequence secondary structure prediction

Protein secondary structure prediction is a subproblem of protein folding. A light-weight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could provide useful input for tertiary structure prediction, alleviating the reliance on multiple sequence alignments typically seen in today's best-performing models. Unfortunately, existing datasets for secondary structure prediction are small, creating a bottleneck. We present PS4, a dataset of 18,731 nonredundant protein chains and their respective secondary structure labels. Each chain is identified, and the dataset is nonredundant against other secondary structure datasets commonly seen in the literature. We perform ablation studies by training secondary structure prediction algorithms on the PS4 training set and obtains state-of-the-art accuracy on the CB513 test set in zero shots.

Maternal oxytocin treatment at birth increases epigenetic age in male offspring

Exogenous oxytocin (OT) is widely used to induce or augment labor with little understanding of the impact on offspring development. In rodent models, including the prairie vole (Microtus ochrogaster), it has been shown that oxytocin administered to mothers can affect the nervous system of the offspring with long lasting behavioral effects especially on sociality. Here, we examined the hypothesis that perinatal oxytocin exposure could have epigenetic and transcriptomic consequences. Prairie voles were exposed to exogenous oxytocin, through injections given to the mother just prior to birth, and were studied at the time of weaning. The outcome of this study revealed increased epigenetic age in oxytocin-exposed animals compared to the saline-exposed group. Oxytocin exposure led to 900 differentially methylated CpG sites (annotated to 589 genes), and 2 CpG sites (2 genes) remained significantly different after correction for multiple comparisons. Differentially methylated CpG sites were enriched in genes known to be involved in regulation of gene expression and neurodevelopment. Using RNA-sequencing we also found 217 nominally differentially expressed genes (p<0.05) in nucleus accumbens, a brain region involved in reward circuitry and social behavior; after corrections for multiple comparisons 6 genes remained significantly differentially expressed. Finally, we found that maternal oxytocin administration led to widespread alternative splicing in the nucleus accumbens. These results indicate that oxytocin exposure during birth may have long lasting epigenetic consequences. A need for further investigation of how oxytocin administration impacts development and behavior throughout the lifespan is supported by these outcomes.

Global profiling of protein lactylation in Caenorhabditis elegans

Lactylation, as a novel posttranslational modification, is essential for studying the functions and regulation of proteins in physiological and pathological processes, as well as for gaining in-depth knowledge on the occurrence and development of many diseases, including tumors. However, few studies have examined the protein lactylation of one whole organism. Thus, we studied the lactylation of global proteins in Caenorhabditis elegans to obtain an in vivo lactylome. Using an MS-based platform, we identified 1836 Class I (localization probabilities > 0.75) lactylated sites in 487 proteins. Bioinformatics analysis showed that lactylated proteins were mainly located in the cytoplasm and involved in the tricarboxylic acid cycle (TCA cycle) and other metabolic pathways. Then, we evaluated the conservation of lactylation in different organisms. In total, 41 C. elegans proteins were lactylated and homologous to lactylated proteins in humans and rats. Moreover, lactylation on H4K80 was conserved in three species. An additional 238 lactylated proteins were identified in C. elegans for the first time. This study establishes the first lactylome database in C. elegans and provides a basis for studying the role of lactylation.

Proteome encoded determinants of protein sorting into extracellular vesicles

Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While EVs and their cargo are promising biomarker candidates, sorting mechanisms of proteins to EVs remain unclear. In this study, we ask if it is possible to determine EV association based on the protein sequence. Additionally, we ask what the most important determinants are for EV association. We answer these questions with explainable AI models, using human proteome data from EV databases to train and validate the model. It is essential to correct the datasets for contaminants introduced by coarse EV isolation workflows and for experimental bias caused by mass spectrometry. In this study, we show that it is indeed possible to predict EV association from the protein sequence: a simple sequence-based model for predicting EV proteins achieved an area under the curve of 0.77 ± 0.01, which increased further to 0.84 ± 0.00 when incorporating curated post-translational modification (PTM) annotations. Feature analysis shows that EV-associated proteins are stable, polar, and structured with low isoelectric point compared to non-EV proteins. PTM annotations emerged as the most important features for correct classification; specifically, palmitoylation is one of the most prevalent EV sorting mechanisms for unique proteins. Palmitoylation and nitrosylation sites are especially prevalent in EV proteins that are determined by very strict isolation protocols, indicating they could potentially serve as quality control criteria for future studies. This computational study offers an effective sequence-based predictor of EV associated proteins with extensive characterisation of the human EV proteome that can explain for individual proteins which factors contribute to their EV association.

A novel homozygous PIGO mutation associated with severe infantile epileptic encephalopathy, profound developmental delay and psychomotor retardation: structural and 3D modelling investigations and genotype-phenotype correlation

The PIGO gene encodes the GPI-ethanolamine phosphate transferase 3, which is crucial for the final synthetic step of the glycosylphosphatidylinositol-anchor serving to attach various proteins to their cell surface. These proteins are intrinsic for normal neuronal and embryonic development. In the current research work, a clinical investigation was conducted on a patient from a consanguineous family suffering from epileptic encephalopathy, characterized by severe seizures, developmental delay, hypotonia, ataxia and hyperphosphatasia. Molecular analysis was performed using Whole Exome Sequencing (WES). The molecular investigation revealed a novel homozygous variant c.1132C > T in the PIGO gene, in which a highly conserved Leucine was changed to a Phenylalanine (p.L378F). To investigate the impact of the non-synonymous mutation, a 3D structural model of the PIGO protein was generated using the AlphaFold protein structure database as a resource for template-based tertiary structure modeling. A structural analysis by applying some bioinformatic tools on both variants 378L and 378F models predicted the pathogenicity of the non-synonymous mutation and its potential functional and structural effects on PIGO protein. We also discussed the phenotypic and genotypic variability associated with the PIGO deficiency. To our best knowledge, this is the first report of a patient diagnosed with infantile epileptic encephalopathy showing a high elevation of serum alkaline phosphatase level. Our findings, therefore, widen the genotype and phenotype spectrum of GPI-anchor deficiencies and broaden the cohort of patients with PIGO associated epileptic encephalopathy with an elevated serum alkaline phosphatase level.

Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive insilico analysis. Among the 319 missense nsSNPs reported, various insilco tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated in vitro to understand the association with some genetic diseases.Communicated by Ramaswamy H. Sarma.

GraphPart: homology partitioning for biological sequence analysis

When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too-closely related pairs of sequences ending up in different partitions. If this is ignored, performance of prediction methods will tend to be overestimated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too-closely related pairs remain. We present GraphPart, an algorithm for homology partitioning that divides the data such that closely related sequences always end up in the same partition, while keeping as many sequences as possible in the dataset. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation on a par with reduction approaches.

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

AIM

Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA-seq) of ALS patient peripheral blood.

METHODS

Whole blood RNA-seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS-control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected.

RESULTS

Two hundred and forty-five differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress-related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects.

CONCLUSIONS

Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.

Understanding the impact of structural modifications at the NNAT gene's post-translational acetylation site: in silico approach for predicting its drug-interaction role in anorexia nervosa

PURPOSE

Anorexia nervosa (AN) is a neuropsychological public health concern with a socially disabling routine and affects a person's healthy relationship with food. The role of the NNAT (Neuronatin) gene in AN is well established. The impact of mutation at the protein's post-translational modification (PTM) site has been exclusively associated with the worsening of the protein's biochemical dynamics.

METHODS

To understand the relationship between genotype and phenotype, it is essential to investigate the appropriate molecular stability of protein required for proper biological functioning. In this regard, we investigated the PTM-acetylation site of the NNAT gene in terms of 19 other specific amino acid probabilities in place of wild type (WT) through various in silico algorithms. Based on the highest pathogenic impact computed through the consensus classifier tool, we generated 3 residue-specific (K59D, P, W) structurally modified 3D models of NNAT. These models were further tested through the AutoDock Vina tool to compute the molecular drug binding affinities and inhibition constant (Ki) of structural variants and WT 3D models.

RESULTS

With trained in silico machine learning algorithms and consensus classifier; the three structural modifications (K59D, P, W), which were also the most deleterious substitution at the acetylation site of the NNAT gene, showed the highest structural destabilization and decreased molecular flexibility. The validation and quality assessment of the 3D model of these structural modifications and WT were performed. They were further docked with drugs used to manage AN, it was found that the ΔGbind (kcal/mol) values and the inhibition constants (Ki) were relatively lower in structurally modified models as compared to WT.

CONCLUSION

We concluded that any future structural variation(s) at the PTM-acetylation site of the NNAT gene due to possible mutational consequences, will serve as a basis to explore its relationship with the propensity of developing AN.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.