Protein Sequence Variation Research Articles

Genetics of Wool and Cashmere Fibre: Progress, Challenges, and Future Research

Wool (sheep) and cashmere (goat) fibres have unique biological, physical, and chemical properties and these fibres are becoming more important as the demand for natural products increases. However, these complex protein fibres are at times compromised by natural variability in their properties, and this can impact their use and value. Genetic improvement via selection and breeding can partly overcome this problem, enabling the farming of sheep and goats that produce more desirable fibre. This review explores the challenges in improving wool and cashmere fibre characteristics using genetics, with a focus on improving our understanding of the key protein components of fibres, wool keratins and keratin-associated proteins (KAPs). Despite progress in our knowledge of these proteins, gaining a better understanding of them and how they affect these fibres remains an ongoing challenge. This is not straight-forward, given the large number of similar yet unique genes that produce the proteins and the gaps that remain in their identification and characterisation. More research is required to clarify gene and protein sequence variability and the location and patterns of gene expression, which in turn limits our understanding of fibre growth and variation. Several aspects that currently hinder our progress in this quest include the incomplete identification of all the genes and weaknesses in the approaches used to characterise them, including newer omics technologies. We describe future research directions and challenges, including the need for ongoing gene identification, variation characterisation, and gene expression analysis and association studies to enable further improvement to these valuable natural fibres.

Functional Identification of Two Glycerol-3-phosphate Acyltransferase5 Homologs from Chenopodium quinoa

Glycerol-3-phosphate acyltransferase5 (GPAT5) is the key enzyme in suberin biosynthesis in Arabidopsis, tomato and Sarracenia purpurea. However, little is known about whether GPAT5 function is conserved in halophytes. In this study, we identified two GPAT5 homologs, CqGPAT5a and CqGPAT5b, in Chenopodium quinoa, the typical halophyte. Using RT-qPCR, we found that CqGPAT5a and CqGPAT5b were highly expressed in quinoa roots and rapidly induced by high salt stress. CqGPAT5a and CqGPAT5b were localized to the endoplasmic reticulum and found to have glycerol-3-phosphate acyltransferase activity using yeast complementation assays. Compared with CqGPAT5b, CqGPAT5a showed relatively weaker function and less protein abundance when expressed in yeast, Arabidopsis or Nicotiana benthamiana. Subsequently, we identified a serine (S) to leucine (L) variation in the CqGPAT5a protein sequence (S251L) compared with CqGPAT5b, located in the connecting region between the second and third transmembrane domains. Site-directed mutagenesis together with yeast mutant complementation and transient expression in tobacco demonstrated that this variation significantly affected CqGPAT5a activity and protein abundance. These findings expand our understanding of GPAT5 and provide new evidence that GPAT5 may be functionally conserved in halophytes.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.

Impact of neutrophil‐activating protein conservation on diagnostic tests and vaccine design

AbstractBACKGROUNDThe neutrophil activating protein (NAP) is a highly immunogenic and virulence factor of Helicobacter pylori, presenting inflammatory and immunomodulatory activity. Consequently, NAP has been explored as a diagnostic and therapeutic target. However, when evaluating a target protein to design diagnostic methods or vaccines, it is critical to determine the protein conservation among the bacterial population, as well the impact of alterations of amino acid residues on the protein antigenic profile.RESULTSIn the present work, NAP conservation and theoretical antigenicity were determined among 51 sequences from H. pylori isolated from patients worldwide. A high NAP conservation (83%) was observed, where 17 amino acid residues, among the 144 residues of the protein, were polymorphic. Alterations at these polymorphic sites had a theoretically low impact on predicted antigenicity, where only 5 NAPs out of 51 NAPs presented a slightly different antigenic profile in relation to the consensus sequence. According to that, it was possible to recognize in western blotting 93% of NAP from different bacteria (n = 15) using polyclonal antibodies developed against a specific NAP.CONCLUSIONSIt was predicted that when working with polyclonal antibodies or large NAP fragments for diagnostic and vaccine design, slight variation in protein sequence will have a minimal impact on NAP recognition. However, if a NAP monoclonal antibody or small NAP epitopes are considered, it is critical to select the most conserved and antigenic NAP regions, to maximize the coverage of NAP variants. © 2024 Society of Chemical Industry (SCI).

Strangers in a foreign land: 'Yeastizing' plant enzymes.

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

An evolutionarily distinct Hmgn2 variant influences shape recognition in Medaka Fish

Protein sequence diversification significantly impacts physiological traits. In this study, using medaka fish (Oryzias latipes), we identify a novel protein variant affecting shape preference behavior. Re-analysis of sequencing data reveals that LOC101156433 encodes a unique Hmgn2 variant with unusual subnuclear localization, clustered separately from the Hmgn2 clades of other species. Medaka mutants with this variant showed reduce telencephalic regions and altered shape preference, suggesting a link between protein sequence variation and behavioral changes. Additionally, this Hmgn2 variant is common in Acanthopterygii fishes, which are adapted to a variety of environments, indicating its potential evolutionary significance. Our findings highlight the relationship between amino acid sequence variation and the development of new molecular and behavioral adaptations, providing insights into the visual shape perception system in fish.

Single Nucleotide Polymorphisms: A Deep Consideration of Protein Sequence Variation

Introduction: Human genome consists of the three billion base pairs that has about one percent of genetic variation from one person to another، which determines physical، psychological، and susceptibility to diseases. Among the types of genetic diversity, single nucleotide polymorphisms are one of the most important genetic differences between two people. Single nucleotide polymorphism variation is located in the promoter region, exons، introns، untranslated regions and other Deoxyribonucleic acid (DNA) regions. While variation in the exon region can change susceptibility to diseases depending on whether it changes the protein structure or affects translation kinetics. Diversity in the promoter region can affect the interaction of genetic and epigenetic elements. Also، variation in the promoter region can affect the DNA methylation status. Polymorphic variation in the intron region can affect Messenger Ribonucleic acid splicing and the function of cis-regulatory elements. Polymorphic variation in the 5' Untranslated region، region causes a change in translation efficiency,، while a change in the 3' Untranslated region binds micro Ribonucleic acids to their position then affects the effects. In some cases، variations in Transfer Ribonucleic acid (tRNA) and Ribosomal ribonucleic acid (rRNA) affect the function of these regulatory cis elements. Conclusion: From a clinical point of view, a deep knowledge of this type of genetic variation can help the treatment process, manage patients and understand the prognosis based on these SNPs. Private or personalized medicine is also fundamentally based on genetic diversity. In this article, it was reviewed the types of single nucleotide genetic variation and presented examples of types of cancer, neurological and immunological diseases.

Molecular dynamics simulations of nucleosomes are coming of age

AbstractUnderstanding the function of eukaryotic genomes, including the human genome, is undoubtedly one of the major scientific challenges of the 21st century. The cornerstone of eukaryotic genome organization is nucleosomes—elementary building blocks of chromatin about 10 nm in size that wrap DNA around an octamer of histone proteins. Nucleosomes are integral players in all genomic processes, including transcription, DNA replication and repair. They mediate genome regulation at the epigenetic level, bridging the discrete nature of the genetic information encoded in DNA with the analog physical nature of the intermolecular interactions required to access that information. Due to their relatively large size and dynamic nature, nucleosomes are difficult objects for experimental characterization. Molecular dynamics (MD) simulations have emerged over the years as a useful tool to complement experimental studies. Particularly in recent years, advances in computing power, refinement of MD force fields and codes have opened up new frontiers in terms of simulation timescales and quality for nucleosomes and related systems. It has become possible to elucidate in atomistic detail their functional dynamics modes such as DNA unwrapping and sliding, to characterize the effects of epigenetic modifications, DNA and protein sequence variation on nucleosome structure and stability, to describe the mechanisms governing nucleosome interactions with chromatin‐associated proteins and the formation of supranucleosome structures. In this review, we systematically analyzed all‐atom MD simulation studies of nucleosomes and related structures published since 2018 and discussed their relevance in the context of older studies, experimental data, and related coarse‐grained and multiscale studies.This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics

Structural Requirements for Reverse Transcription by a Diversity-generating Retroelement.

Diversity-generating retroelements (DGRs) create massive protein sequence variation in ecologically diverse microbes. Variation occurs during reverse transcription of a protein-encoding RNA template coupled to misincorporation at adenosines. In the prototypical Bordetella bacteriophage DGR, the template must be surrounded by upstream and downstream RNA segments for cDNA synthesis by the reverse transcriptase bRT and associated protein Avd. The function of the surrounding RNA was unknown. Cryo-EM revealed that this RNA enveloped bRT and lay over barrel-shaped Avd, forming an intimate ribonucleoprotein (RNP). An abundance of essential interactions between RNA structural elements and bRT-Avd precisely positioned an RNA homoduplex for initiation of cDNA synthesis by cis -priming. Our results explain how the surrounding RNA primes cDNA synthesis, promotes processivity, terminates polymerization, and strictly limits mutagenesis to select proteins through mechanisms that are likely conserved in DGRs from distant taxa.

A Systematic Bioinformatics Approach for Mapping the Minimal Set of a Viral Peptidome

AbstractSequence changes in viral genomes generate protein sequence diversity that enables viruses to evade the host immune system, hindering the development of effective preventive and therapeutic interventions. The massive proliferation of sequence data provides unprecedented opportunities to study viral adaptation and evolution. An alignment‐free approach removes various restrictions posed by an alignment‐dependent approach for studying sequence diversity. The publicly available tool, UNIQmin, offers an alignment‐free approach for studying viral sequence diversity at any given rank of taxonomy lineage and is big data ready. The tool performs an exhaustive search to determine the minimal set of sequences required to capture the peptidome diversity within a given dataset. This compression is possible through the removal of identical sequences and unique sequences that do not contribute effectively to the peptidome diversity pool. Herein, we describe a detailed four‐part protocol utilizing UNIQmin to generate the minimal set for the purpose of viral diversity analyses, alignment‐free at any rank of the taxonomy lineage, using the recent global public health threat Monkeypox virus (MPX) sequence data as a case study. The protocol enables a systematic bioinformatics approach to study sequence diversity across taxonomic lineages, which is crucial for our future preparedness against viral epidemics. This is particularly important when data are abundant, freely available, and alignment is not an option. © 2024 Wiley Periodicals LLC.Basic Protocol 1: Tool installation and input file preparationBasic Protocol 2: Generation of a minimal set of sequences for a given datasetBasic Protocol 3: Comparative minimal set analysis across taxonomic lineage ranksBasic Protocol 4: Factors affecting the minimal set of sequences

Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel.

In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.

First vertebrate BRICHOS antimicrobial peptides: β-hairpin host defense peptides in limbless amphibia lung resemble those of marine worms

Innate immunity of invertebrates offers potent antimicrobial peptides (AMPs) against drug-resistant infections. To identify new worm β-hairpin AMPs, we explored the sequence diversity of proteins with a BRICHOS domain, which comprises worm AMP precursors. Strikingly, we discovered new BRICHOS AMPs not in worms, but in caecilians, the least studied clade of vertebrates. Two precursor proteins from Microcaecilia unicolor and Rhinatrema bivittatum resemble SP-C lung surfactants and bear worm AMP-like peptides at C-termini. The analysis of M. unicolor tissue transcriptomes shows that the AMP precursor is highly expressed in the lung along with regular SP-C, suggesting a different, protective function. The peptides form right-twisted β-hairpins, change conformation upon lipid binding, and rapidly disrupt bacterial membranes. Both peptides exhibit broad-spectrum activity against multidrug-resistant ESKAPE pathogens with 1–4 μM MICs and remarkably low toxicity, giving 40–70-fold selectivity towards bacteria. These BRICHOS AMPs, previously unseen in vertebrates, reveal a novel lung innate immunity mechanism and offer a promising antibiotics template.

Abstract 4345: Evaluation of the prevalence and impact of molecular variation in the three-prime repair exonuclease 1 TREX1 across mammalian species and in human malignancies

Abstract The three-prime repair exonuclease 1, TREX1, degrades cytosolic DNA to prevent aberrant immune activation. Inactivation of TREX1 results in DNA accumulation in the cytosol and subsequent induction of the cGAS-STING DNA sensing pathway, downstream interferon-mediated innate immune signaling, and inflammation. Correspondingly, germline pathogenic mutational defects in the TREX1 gene lead to hereditary autoimmune and autoinflammatory disorders. While TREX1 mutations are also reported in some tumors, their consequences in cancer remain poorly understood. To assess the functional importance of amino acid residues in TREX1, we analyzed protein sequences of the functional TREX1 isoform, TREX1b, from 168 mammalian species. We also conducted a survey of genomic variant databases COSMIC, ClinVar, TCGA, LOVD, Decipher, and gnomAD for disease-associated TREX1 mutations. Finally, we modeled TREX1 mutations occurring in human tumors to evaluate predicted impact on protein stability and function using structure-based approaches FoldX and INPS-3D, and compared results to available predictions from AlphaMissense and COSMIC-3D. In the Cancer Genome Atlas (TCGA), 16 out of 32 cancer histologies have tumor samples bearing protein-altering TREX1 variants, with bladder cancer and melanoma having the highest frequency of TREX1 variants at 2.7% of tumor samples. Moreover, all 32 cancer types have some samples with loss of one or both copies of TREX1; frequencies of heterozygous loss range from 0.6% (thymoma) to 86% (kidney clear cell and lung squamous cell carcinomas). We also observed TREX1 copy gain with lower frequency than instances of copy loss, but also occurring in 31 out of 32 studies. Multiple tumors have a combination of a TREX1 protein sequence variant and copy number gain or loss, suggesting that their potential interplay may increase functional impact of TREX1 variants. Out of 15 cancer-associated TREX1 variants, 5 (33%) were predicted to be pathogenic by AlphaMissense. For FoldX and INPS-3D mutagenesis modeling, we used the published experimental 3D crystal structure of the wild type (WT) human TREX1 (PDB: 7TQQ). The changes in Gibbs free energy of folding ΔΔG between WT and mutant predicted by FoldX and INPS-3D both correlated strongly with the AlphaMissense pathogenic probability scores (Pearson r=0.69 and 0.73, respectively). We will discuss implications of TREX1 variation and conservation across mammalian species and potential effects of germline TREX1 variants in hereditary autoimmune and autoinflammatory disorders and on somatic and germline TREX1 variants in human cancer cells and tumor samples. Citation Format: Mariam M. Konate, Marwa Shekfeh, Julia Krushkal. Evaluation of the prevalence and impact of molecular variation in the three-prime repair exonuclease 1 TREX1 across mammalian species and in human malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4345.

A pilot investigation of the association between HIV-1 Vpr amino acid sequence diversity and the tryptophan-kynurenine pathway as a potential mechanism for neurocognitive impairment

HIV infection compromises both the peripheral and central immune systems due to its pathogenic and neuropathogenic features. The mechanisms driving HIV-1 pathogenesis and neuropathogenesis involve a series of events, including metabolic dysregulation. Furthermore, HIV-subtype-specific variations, particularly alterations in the amino acid sequences of key viral proteins, are known to influence the severity of clinical outcomes in people living with HIV. However, the impact of amino acid sequence variations in specific viral proteins, such as Viral protein R (Vpr), on metabolites within the Tryptophan (Trp)-kynurenine (Kyn) pathway in people living with HIV remains unclear. Our research aimed to explore the relationship between variations in the Vpr amino acid sequence (specifically at positions 22, 41, 45, and 55, as these have been previously linked to neurocognitive function) and peripheral Trp-Kyn metabolites. Additionally, we sought to clarify the systems biology of Vpr sequence variation by examining the link between Trp-Kyn metabolism and peripheral inflammation, as a neuropathogenic mechanism. In this preliminary study, we analyzed a unique cohort of thirty-two (n = 32) South African cART naïve people living with HIV. We employed Sanger sequencing to ascertain blood-derived Vpr amino acid sequence variations and a targeted LC-MS/MS metabolomics platform to assess Trp-Kyn metabolites, such as Trp, Kyn, kynurenic acid (KA), and quinolinic acid (QUIN). Particle-enhanced turbidimetric assay and Enzyme-linked immunosorbent assays were used to measure immune markers, hsCRP, IL-6, suPAR, NGAL and sCD163. After applying Bonferroni corrections (p =.05/3) and adjusting for covariates (age and sex), only the Vpr G41 and A55 groups was nearing significance for higher levels of QUIN compared to the Vpr S41 and T55 groups, respectively (all p =.023). Multiple regression results revealed that Vpr amino acid variations at position 41 (adj R2 = 0.049, β = 0.505; p =.023), and 55 (adj R2 = 0.126, β = 0.444; p =.023) displayed significant associations with QUIN after adjusting for age and sex. Lastly, the higher QUIN levels observed in the Vpr G41 group were found to be correlated with suPAR (r =.588, p =.005). These results collectively underscore the importance of specific Vpr amino acid substitutions in influencing QUIN and inflammation (specifically suPAR levels), potentially contributing to our understanding of their roles in the pathogenesis and neuropathogenesis of HIV-1.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

High-Temperature Tolerance Protein Engineering through Deep Evolution

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

Pan-Genome Analysis of TIFY Gene Family and Functional Analysis of CsTIFY Genes in Cucumber.

Cucumbers are frequently affected by gray mold pathogen Botrytis cinerea, a pathogen that causes inhibited growth and reduced yield. Jasmonic acid (JA) plays a primary role in plant responses to biotic stresses, and the jasmonate-ZIM-Domain (JAZ) proteins are key regulators of the JA signaling pathway. In this study, we used the pan-genome of twelve cucumber varieties to identify cucumber TIFY genes. Our findings revealed that two CsTIFY genes were present in all twelve cucumber varieties and showed no differences in protein sequence, gene structure, and motif composition. This suggests their evolutionary conservation across different cucumber varieties and implies that they may play a crucial role in cucumber growth. On the other hand, the other fourteen CsTIFY genes exhibited variations in protein sequence and gene structure or conserved motifs, which could be the result of divergent evolution, as these genes adapt to different cultivation and environmental conditions. Analysis of the expression profiles of the CsTIFY genes showed differential regulation by B. cinerea. Transient transfection plants overexpressing CsJAZ2, CsJAZ6, or CsZML2 were found to be more susceptible to B. cinerea infection compared to control plants. Furthermore, these plants infected by the pathogen showed lower levels of the enzymatic activities of POD, SOD and CAT. Importantly, after B. cinerea infection, the content of JA was upregulated in the plants, and cucumber cotyledons pretreated with exogenous MeJA displayed increased resistance to B. cinerea infection compared to those pretreated with water. Therefore, this study explored key TIFY genes in the regulation of cucumber growth and adaptability to different cultivation environments based on bioinformatics analysis and demonstrated that CsJAZs negatively regulate cucumber disease resistance to gray mold via multiple signaling pathways.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

HIV-1 Vif protein sequence variations in South African people living with HIV and their influence on Vif-APOBEC3G interaction

PurposeDespite extensive research, HIV-1 remains a global epidemic with variations in pathogenesis across regions and subtypes. The Viral Infectivity Factor (Vif) protein, which neutralizes the host protein APOBEC3G, has been implicated in differences in clinical outcomes among people living with HIV (PLHIV). Most studies on Vif sequence diversity have focused on subtype B, leaving gaps in understanding Vif variations in HIV-1C regions like South Africa. This study aimed to identify and compare Vif sequence diversity in a cohort of 51 South African PLHIV and other HIV-1C prevalent regions.MethodsSanger sequencing was used for Vif analysis in the cohort, and additional sequences were obtained from the Los Alamos database. Molecular modeling and docking techniques were employed to study the influence of subtype-specific variants on Vif-APOBEC3G binding affinity.ResultsThe findings showed distinct genetic variations between Vif sequences from India and Uganda, while South African sequences had wider distribution and closer relatedness to both. Specific amino acid substitutions in Vif were associated with geographic groups. Molecular modeling and docking analyses consistently identified specific residues (ARGR19, LYS26, TYR30, TYR44, and TRP79) as primary contributors to intermolecular contacts between Vif and APOBEC3G, essential for their interaction. The Indian Vif variant exhibited the highest predicted binding affinity to APOBEC3G among the studied groups.ConclusionsThese results provide insights into Vif sequence diversity in HIV-1C prevalent regions and shed light on differential pathogenesis observed in different geographical areas. The identified Vif amino acid residues warrant further investigation for their diagnostic, prognostic, and therapeutic potential.