Amino Acid Distances Research Articles

Novel Amino Acid Distance Matrices based on Conductance Measure

Ancestral relationships among biological species are often represented and analyzed by means of phylogenetic trees. Substitution and distance matrices are two main types of matrices that are used in phylogeny analyses. Substitution matrices describe a frequency change of amino acids in nucleotide or protein sequence over time, while distance matrices estimate phylogeny using a matrix of pairwise distances based on a particular code or analytical concept. Recent investigation by Elena Fimmel and coworkers (Life 11:1338, 2021) showed that: 1. the robustness of a genetic code against point mutations can be described using the conductance measure, and 2. all possible point mutations of the genetic code can be represented as a weighted graph with weights that correspond to the probabilities of these mutations. In this article, we constructed and tested three novel distance matrices based on conductance measure, that take into account the point mutation robustness of the Standard Genetic Code (SGC). These distance matrices are based on maximum (CMAX), average (CAVG), and minimum (CMIN) conductance-optimized distances between codons coding for individual amino acids. The performance of those distance matrices was tested on a dataset of RecA proteins in Bacteria, Archaea (RadA homolog) and Eukarya (Rad51 homolog). RecA protein and its functional homologs were selected for this investigation since they are essential for the repair and maintenance of DNA, and consequently well conserved and present in all domains of life. PAM250 and BLOSUM62 matrices were usually used as a standard for distance matrix testing. PAM250 and BLOSUM62 substitution matrices specified accurately three biological domains of life according to Carl Woese and George Fox (Proc Natl Acad Sci U S A 74:5088, 1977). An identical result was obtained using three novel distance matrices (CMIN, CMAX, CAVG). This result supports the applicability of novel distance matrices based on the conductance method, and suggests that further investigations based on this approach are justified.

Genetic Links between Reproductive Traits and Amino Acid Pairwise Distances of Swine Leukocyte Antigen Alleles among Mating Partners in Microminipigs.

Previously, we found that a greater dissimilarity in swine leukocyte antigen (SLA) class I and class II alleles between mating partners resulted in increased farrowing rates in a highly inbred population of Microminipigs (MMPs). In this follow-up study, we have analyzed the effects of dissimilarity in SLA alleles between mating partners for seven different reproductive traits, including litter size and the number of stillborn and live or dead weaned piglets. We determined the relationships among reproductive traits within each mating event and the amino acid distances of SLA alleles as markers of diversity between mating partners. Our results indicate that mating partners with greater amino acid pairwise genetic distances in the SLA-1 class I gene or DQB1 class II gene alleles were associated with significantly larger litter sizes and higher numbers of live piglets at birth and weaning. Also, partners with greater pairwise distances in the SLA-2 class I gene alleles exhibited fewer pre-weaning deaths. These findings suggest that the dissimilarity in SLA class I and class II alleles between mating partners may affect not only farrowing rates but also other key reproductive traits such as litter size and improved piglet survival rates. Consequently, SLA alleles could serve as valuable genetic markers for selecting mating partners in breeding programs and for conducting epistatic studies on various reproductive traits in MMPs.

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

BackgroundProtein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.ResultsIn this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.ConclusionsGATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol.

Diversity and Evolutionary History of Ti Plasmids of "tumorigenes" Clade of Rhizobium spp. and Their Differentiation from Other Ti and Ri Plasmids.

Agrobacteria are important plant pathogens responsible for crown/cane gall and hairy root diseases. Crown/cane gall disease is associated with strains carrying tumor-inducing (Ti) plasmids, while hairy root disease is caused by strains harboring root-inducing (Ri) plasmids. In this study, we analyzed the sequences of Ti plasmids of the novel "tumorigenes" clade of the family Rhizobiaceae ("tumorigenes" Ti plasmids), which includes two species, Rhizobium tumorigenes and Rhizobium rhododendri. The sequences of reference Ti/Ri plasmids were also included, which was followed by a comparative analysis of their backbone and accessory regions. The "tumorigenes" Ti plasmids have novel opine signatures compared with other Ti/Ri plasmids characterized so far. The first group exemplified by pTi1078 is associated with production of agrocinopine, nopaline, and ridéopine in plant tumors, while the second group comprising pTi6.2 is responsible for synthesis of leucinopine. Bioinformatic and chemical analyses, including opine utilization assays, indicated that leucinopine associated with pTi6.2 most likely has D,L stereochemistry, unlike the L,L-leucinopine produced in tumors induced by reference strains Chry5 and Bo542. Most of the "tumorigenes" Ti plasmids have conjugative transfer system genes that are unusual for Ti plasmids, composed of avhD4/avhB and traA/mobC/parA regions. Next, our results suggested that "tumorigenes" Ti plasmids have a common origin, but they diverged through large-scale recombination events, through recombination with single or multiple distinct Ti/Ri plasmids. Lastly, we showed that Ti/Ri plasmids could be differentiated based on pairwise Mash or average amino-acid identity distance clustering, and we supply a script to facilitate application of the former approach by other researchers.

What geometrically constrained models can tell us about real-world protein contact maps

The mechanisms by which a protein’s 3D structure can be determined based on its amino acid sequence have long been one of the key mysteries of biophysics. Often simplistic models, such as those derived from geometric constraints, capture bulk real-world 3D protein-protein properties well. One approach is using protein contact maps (PCMs) to better understand proteins’ properties. In this study, we explore the emergent behaviour of contact maps for different geometrically constrained models and compare them to real-world protein systems. Specifically, we derive an analytical approximation for the distribution of amino acid distances, denoted as P(s), using a mean-field approach based on a geometric constraint model. This approximation is then validated for amino acid distance distributions generated from a 2D and 3D version of the geometrically constrained random interaction model. For real protein data, we show how the analytical approximation can be used to fit amino acid distance distributions of protein chain lengths of L ≈ 100, L ≈ 200, and L ≈ 300 generated from two different methods of evaluating a PCM, a simple cutoff based method and a shadow map based method. We present evidence that geometric constraints are sufficient to model the amino acid distance distributions of protein chains in bulk and amino acid sequences only play a secondary role, regardless of the definition of the PCM.

Viral Protein 1 (VP1) Sequence-Based Genetic Diversity of SAT 2 FMDV Circulating in Ethiopia from 1990 to 2015.

Pathogen molecular epidemiology determines the origin of specific outbreaks locality of foot-and-mouth disease virus serotype South African Territories-2 sequence-based analysis of highly variable Viral Protein 1 (VP1), which helps to identify the evolution of this virus through time and space. The objective of this study was to compare the differences between SAT-2 VP1 sequences of FMDV circulated in Ethiopia from 1990 to 2015 at the genetic level. The nucleotide and amino acid sequences were analyzed using Basic Local Alignment Search Tools (BLAST), Multiple sequence alignment and sequence editing and Phylogenetic tree reconstruction. The nucleotide and amino acid sequences alignment, distance matrix, and phylogenetic tree constructions were done using the MEGA 6.0 software package. In this analysis, we found 76% nucleotide identities and amino acid similarities among the sequences. The overall group mean distance at nucleotide level was 19% with a mean intra-population diversity of 2%. The lowest sequence variation was observed among sequences obtained from the years 2007/09/10, 2014/15, and 1990/91 which was less than 5% among them. This analysis revealed that in the last 25 years, four different topotypes of the FMDV SAT-2 were circulating in Ethiopia. The Arg-Gly-Asp (RGD) amino acid (AA) motif at AA position 144-146 within the G-H loop of the VP1 protein of FMDV is conserved, but up- and downstream hyper-variable AA sequences are identified. In this study, it was observed that four topotypes (IV, XIV, XIII, and VII) were circulating in Ethiopia for 25 years. Further, compared with sequences from neighboring countries (Sudan, Kenya) confirmed the presence of these topotypes. Pertinent to this genetic diversity control strategies in Ethiopia should be based on having regular antigenic and genetic vaccine matching tests with the circulating strain within a defined period, space, transboundary nature of the disease and applying biosecurity measures.

IN SILICO STUDY OF PHYTOCONSTITUENTS OF MORINGA OLEIFERA AGAINST TREHALOSE–6–PHOSPHATE PHOSPHATASE (TPP) ENZYMES

Numerous pathogenic microorganisms use the enzymes trehalose-6-phosphate phosphatase (TPP) to biosynthesize the sugar trehalose from trehalose-6-phosphate (T6P) as part of their infection and proliferation processes. In order to build new generation candidate medications to inhibit TPP using in silico approaches, the current work is being done. The majority of the 10 phytochemicals from Moringa oleifera that were used in docking studies with the 3D model of TPP had good binding affinities to the enzyme, with campesterol and stigmat-4-en-3-one showing the highest affinities (Binding energies of - 7.8 kcal/mole and -8.0 kcal/mole, respectively), when compared to the commonly used commercial drug isoniazid (Binding energy of –6.0 kcal/mole).The active site of TPP sequences, which coordinates Mg2+ and is necessary for catalysis, has been discovered to bind to phytochemical leads during docking. Binding poses and distance measurement of TPP-phytochemical complexes of 5-Hydroxymethylfurfural, Citronellol, Xylitol, 2,4-Di-tert-butylphenol, Palmitoleic acid, cis-Vaccenic acid, Phytol, Cyclopentane, 1,1-[3-(2-cyclopentylethyl)-1,5-pentanediyl]bis-, Campesterol and Stigmat-4-en-3-one reveals that the lead phytochemicals were in close proximity with most of the active site amino acids (distance range from 1.88 to 3.77 Aº). This demonstrates the close affinity between the enzyme and the leads, which may open the door to the development of new generation medications to treat diseases caused by pathogenic microorganisms that produce TPP. KEYWORDS: Trehalose–6–phosphate phosphatase, Binding affinity, In silico, Phytochemical, Moringa oleifera

Protein network centralities as descriptor for QM region construction in QM/MM simulations of enzymes.

The construction of a suitable QM region is the most crucial step in setting up hybrid quantum mechanics/molecular mechanics (QM/MM) simulations for enzymatic reactions. The QM region should ideally include all important amino acids residues, while being as small as possible to save computational effort. Most available methods for systematic QM region construction are based either on the distance of single amino acids to the active site or on their electrostatic effect. Such approaches might miss non-electrostatic and long-range allosteric interactions. Here, we present a proof of concept study for the application of protein network analysis to tackle this problem. Specifically, we explore the use of the protein network centralities as descriptor for QM region construction. We find that protein network centralities, in particular the betweenness centrality, can be a useful descriptor for systematic QM region construction. We show that in combination with our previously developed point charge variation analysis, they can be used to identify important residues that are missed in purely electrostatic approaches.

Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.

The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico, based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% (95% confidence interval, 76 to 85%); sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains. IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others.

High-resolution mass spectrometry unveils the molecular changes of ovalbumin induced by heating and their influence on IgE binding capacity

Ovalbumin (OVA) is a food allergen whose allergenicity is modulated by heating. We aimed to establish a molecular connection between heat-induced structural modifications and the modulation of the IgE binding capacity of OVA. For this, we used model samples of heat-modified OVA with increasing complexity; glycated, aggregated, or glycated and aggregated. Using sera from egg-allergic individuals, we show that both aggregation and glycation strongly impacted IgE binding capacity, despite limited structural changes for glycated OVA. A molecular exploration at the amino acid level using high-resolution mass spectrometry revealed extensive cross-linking, mostly through disulfide and dehydroprotein bridges, and moderate but significant glycation. Structural modifications affected residues located within or at a few amino acids distance of known human linear IgE epitopes, such as C121, K123, S169, K190, K207, H332 and C368. We thus unveil key amino residues implicated in the changes in IgE binding of OVA induced by heating.

SimPlot++: a Python application for representing sequence similarity and detecting recombination.

Accurate detection of sequence similarity and homologous recombination are essential parts of many evolutionary analyses. We have developed SimPlot++, an open-source multiplatform application implemented in Python, which can be used to produce publication quality sequence similarity plots using 63 nucleotide and 20 amino acid distance models, to detect intergenic and intragenic recombination events using Φ, Max-χ2, NSS or proportion tests, and to generate and analyze interactive sequence similarity networks. SimPlot++ supports multicore data processing and provides useful distance calculability diagnostics. SimPlot++ is freely available on GitHub at: https://github.com/Stephane-S/Simplot_PlusPlus, as both an executable file (for Windows) and Python scripts (for Windows/Linux/MacOS).

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.

Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.

Effect of pH on lipid oxidation mediated by hemoglobin in washed chicken muscle

To investigate the effect of pH on lipid oxidation of chicken muscle, chicken hemolysates were added to washed chicken muscles to analyze lipid oxidation at pH 5.7, 6.3, and 7.2. The results showed that with a blue shift of the Soret peak, oxyhemoglobin gradually transformed to methemoglobin during storage, the shape of porphyrin rings of heme in fluorescence electron microscopy changed from round to trail-like structure. These changes were more significant at low pH. Comparing hemoglobin (Hb) structure, the distance ofamino acids between the E10 of lysine and metHb-7-propionate groups is longer at pH 5.7 than other pHs, which makes solvent easily enter the heme cavity, leading tothe severe destruction of Hb. The linear correlation between color and lipid oxidation also further confirmed that the increased oxidation of chicken Hb causes more rapid lipid oxidation in pH 5.7 than the other 2 pHs (p < 0.05).

Monsavirus in monkey rectal swab and throat swab specimens in China: Proposal for Posaliviridae as a new family in Picornavirales

Posa-like viruses have been detected in the fecal samples of several host species and are considered unclassified members of Picornavirales. Here, we identified genomic fragments of novel posa-like viruses (monsaviruses) in monkey specimens through next generation sequencing and obtained 11 full-length genomes. This monsavirus shared 88.5–89.2% nucleotide similarity with the Tottori-HG1 strain (GenBank accession LC123275). In total, 713 nucleotide polymorphism sites were identified, indicating their persistent evolution during circulation. The genomic organization and phylogenetic relationship of monsavirus were determined. Subsequent phylogenetic analysis of the conserved replication block of Hel-Pro-RdRp and core RNA-dependent RNA polymerase domain-based analysis of posa-like viruses showed significant separation compared with other known families. Further, posa-like virus genomes possessed the classical replication block of picornavirus in the 5′ part of genome and picorna-like capsid domains at the structural coding region of 3′ part of genome. Based on these results, we proposed the new family Posaliviridae, within Picornavirales. Four genera, which showed 68.6–75.5% amino acid distances but similar genomic organization including the conserved replication block of Hel-Pro-RdRp, the same order of the genomic coding region, and picorna-like capsid domains, were identified. The flexible genomic organization strategy and a large evolutionary scale of Posaliviridae was explicit. This study provides novel information on monsaviruses and important taxonomic data for the family Posaliviridae.

Total Ortholog Median Matrix as an alternative unsupervised approach for phylogenomics based on evolutionary distance between protein coding genes

The increasing number of available genomic data allowed the development of phylogenomic analytical tools. Current methods compile information from single gene phylogenies, whether based on topologies or multiple sequence alignments. Generally, phylogenomic analyses elect gene families or genomic regions to construct phylogenomic trees. Here, we presented an alternative approach for Phylogenomics, named TOMM (Total Ortholog Median Matrix), to construct a representative phylogram composed by amino acid distance measures of all pairwise ortholog protein sequence pairs from desired species inside a group of organisms. The procedure is divided two main steps, (1) ortholog detection and (2) creation of a matrix with the median amino acid distance measures of all pairwise orthologous sequences. We tested this approach within three different group of organisms: Kinetoplastida protozoa, hematophagous Diptera vectors and Primates. Our approach was robust and efficacious to reconstruct the phylogenetic relationships for the three groups. Moreover, novel branch topologies could be achieved, providing insights about some phylogenetic relationships between some taxa.

Peptide bond planarity constrains hydrogen bond geometry and influences secondary structure conformations.

An extensive database study of hydrogen bonds in different protein environments showed systematic variations in donor-acceptor-acceptor antecedent angle (Ĥ) and donor-acceptor distance. Protein environments were characterized by depth (distance of amino acids from bulk solvent), secondary structure, and whether the donor/acceptor belongs to the main chain (MC) or side chain (SC) of amino acids. The MC-MC hydrogen bonds (whether in secondary structures or not) have Ĥ angles tightly restricted to a value of around 155°, which was distinctly different from other Ĥ angles. Quantum chemical calculations attribute this characteristic MC-MC Ĥ angle to the nature of the electron density distribution around the planar peptide bond. Additional classical simulations suggest a causal link between MC-MC Ĥ angle and the conformation of secondary structures in proteins. We also showed that donor-acceptor distances are environment dependent, which has implications on protein stability. Our results redefine hydrogen bond geometries in proteins and suggest useful refinements to existing molecular mechanics force fields.

Anacardic Acid Complexes as Possible Agents Against Alzheimer's Disease Through Their Antioxidant, In vitro, and In silico Anticholinesterase and Ansiolic Actions.

The frequency of Alzheimer's disease (AD) is growing rapidly with longer life expectancy and the consequent increase of people with a high risk of neurodegenerative diseases. Anacardic acid (AA) has several pharmacological actions, such as antioxidants, anticholinesterase, and anti-inflammatory, which are related to the protection against aging disorders. Also, the metals copper and zinc are co-factors of antioxidant enzymes that can be associated with AA to improve brain-protective action. This study aimed to evaluate the potential of AA metal complexes using copper and zinc chelators to produce potential agents against Alzheimer's disease. For this purpose, Cu and Zn were linked to AA in the ratio of 1:1 in a basic medium. The complexes' formation was confirmed by ultraviolet and visible spectroscopy. The toxicity was evaluated in the zebrafish model, and other information related to AD was obtained using the zebrafish model of anxiety. AA-Zn and AA-Cu complexes showed better antioxidant action than free AA. In the anti-AChE activity, AA was like the AA-Cu complex. In models using adult zebrafish, no toxicity for AA complexes was found, and in the locomotor model, AA-Cu demonstrated possible anxiolytic action. In in silico experiments comparing AA and AA-Cu complex, the coupling energy with the enzyme was lower for the AA-Cu complex, showing better interaction, and also the distances of the active site amino acids with this complex were lower, similar to galantamine, the standard anti-AChE inhibitor. Thus, AA-Cu showed interesting results for more detailed study in experiments related to Alzheimer's disease.

Epitope-based sieve analysis of Plasmodium falciparum sequences from a FMP2.1/AS02A vaccine trial is consistent with differential vaccine efficacy against immunologically relevant AMA1 variants

To prevent premature dismissal of promising vaccine programs, it is critical to determine if lack of efficacy in the field is due to allele specific-efficacy, rather than to the lack of immunogenicity of the candidate antigen. Here we use samples collected during a field trial of the AMA1-based FMP2.1/AS02A malaria vaccine, which incorporates the AMA1 variant encoded by the reference Plasmodium falciparum 3D7 strain, to assess the usefulness of epitope-based sieve analysis for the detection of vaccine-induced allele-specific immune responses.The samples used are from volunteers who received the malaria vaccine FMP2.1/AS02A or a control (rabies vaccine), during a vaccine efficacy field trial, and who later developed malaria. In a previous study, P. falciparum DNA was extracted from all samples, and the ama1 locus amplified and sequenced. Here, a sieve analysis was used to measure T and B-cell escape, and difference in 3D7-like epitopes in the two treatment arms.Overall, no difference was observed in mean amino acid distance to the 3D7 AMA1 variant between sequences from vaccinees and controls in B-cell epitopes. However, we found a significantly greater proportion of 3D7-like T-cell epitopes that map to the AMA1 cluster one loop (c1L) region in the control vs. the vaccinee group (p = 0.02), consistent with allele-specific vaccine efficacy. Interestingly, AMA1 epitopes in infections from vaccinees had higher mean IC50, and consequently lower binding affinity, than epitopes generated from the control group (p = 0.01), suggesting that vaccine-induced selection impacted the immunological profile of the strains that pass through the sieve imposed by the vaccine-induced protection. These findings are consistent with a vaccine-derived sieve effect on the c1L region of AMA1 and suggest that sieve analyses of malaria vaccine trial samples targeted to epitopes identified in silico can help identify protective malaria antigens that may be efficacious if combined in a multivalent vaccine.

Analysis of Vpr Genetic Variations between Chinese Major Circulating Recombinants CRF01_AE and CRF07_BC.

HIV-1 CRF01_AE and CRF07_BC recombinant strains are responsible for more than 80% of new infections in China since the beginning of the 2000s. These two strains may have distinct genetic mutations, which resulted in distinct patterns of pathogenesis related to the viral gene, Vpr. The amino acid pattern and genetic diversity of Vpr were analyzed and characterized in HIV-1 CRF01_AE and CRF07_BC HIV-1 strains. The Vpr gene was amplified from extracted viral RNA and DNA sequencing was performed using an ABI3730 analyzer. The positional amino acid composition, genetic variation and distance of Vpr sequence were analyzed by Bio-Edit 7.2 and Mega 6.01 software packages. A total of 162 CRF01_AE and 80 CRF07_BC derived Vpr sequences were obtained by DNA sequencing. CRF01_AE patients showed higher viral load and lower CD4 counts than CRF07_BC patients (P<0.05). Higher genetic distance and more polymorphic amino acids were found in CRF01_AE Vpr than CRF07_BC Vpr (P<0.05). The common conservative amino acid region was identified as 29EAVRHFP35 in both CRF07_BC and CRF01_AE. Of note, the R77Q mutation was found in both the most recently Chinese derived CRF07_BC and CRF01_AE Vpr. CRF01_AE derived Vpr has higher genetic variation and pathogenesis in comparison to the CRF07_BC strain.

SAT-669 CD40 Ligand Gene Mutation in Type 1 Diabetes Mellitus in a Saudi Consanguineous Family

Introduction:Type 1 Diabetes Mellitus (T1D) is a common autoimmune disorder. Investigating genetic factors that could turn the immune cells to auto-reactive are critical to our understanding of T1D. In this study genetic factors and the affected autoimmunity related molecular mechanisms in familial T1D with parental consanguinity were studied.Materials and Method:Whole Exome Sequencing (WES) was performed in a family with familial T1D. Sanger Sequencing was done to analyze segregation in affected and non-affected. Clinical and molecular aspects were also evaluated. Protein modeling and other in silico tools were used to identify probable impact of genetic abnormalities on the structure and function of protein.Result:We identified a novel homozygous substitution mutation at “c.379A>T:p.Iso127Val” in CD40 Ligand (CD40LG) gene in diabetic siblings by WES. This change was found to be segregating in the affected and non-affected individuals by Sanger sequencing. Parents and non-diabetic siblings were found to be heterozygous carriers of the nucleotide change. The c.379A>T is located within evolutionarily conserved locus of human genome. Functional prediction by Gene Ontology term analysis suggested that immune functions of CD40LG are compromised by this genetic change. Further, by protein-modeling analysis we identified that structure of ligand-binding domain of CD40LG protein, with which it interacts with other immune cells, may also be affected. In silico analysis revealed significantly reduced spatial inter amino acid distance between the site of genetic change and the ligand binding domain in mutant CD40LG.Discussion:Apoptotic elimination of developing auto-reactive T-cells, having the potential to generate an autoimmune response against pancreatic cells, is critical to prevent T1D. This apoptotic removal mechanisms, occurs in thymus and is dependent on highly specific interaction of cell surface molecules as CD40LG on developing T-cells, and the corresponding cell surface molecules on Antigen Presenting Cells (APC). In the diabetic individuals investigated in our study, the mutation in CD40LG gene, caused significant structural damage to its protein, due to which these interactions between mutant CD40LG (present on T-cells) and the corresponding CD40 (present on APC) were probably affected. This loss of interaction between CD40LG bearing T-cells and APC, probably led to escape of auto-reactive T-cells in to immune system, which further generated autoimmune response against the pancreatic tissue, precipitating to T1D among these individuals.ConclusionGenetic investigation of cell surface proteins in autoimmune cells and understanding their mechanism for development of autoimmunity offers prospects for the development of new therapeutic strategies in the approach to probable early diagnosis of T1D.