Structural Modeling Tool Research Articles

Exploring structure-function relationships in engineered receptor performance using computational structure prediction.

Engineered receptors play increasingly important roles in transformative cell-based therapies. However, the structural mechanisms that drive differences in performance across receptor designs are often poorly understood. Recent advances in protein structural prediction tools have enabled the modeling of virtually any user-defined protein, but how these tools might build understanding of engineered receptors has yet to be fully explored. In this study, we employed structural modeling tools to perform post hoc analyses to investigate whether predicted structural features might explain observed functional variation. We selected a recently reported library of receptors derived from natural cytokine receptors as a case study, generated structural models, and from these predictions quantified a set of structural features that plausibly impact receptor performance. Encouragingly, for a subset of receptors, structural features explained considerable variation in performance, and trends were largely conserved across structurally diverse receptor sets. This work indicates potential for structure prediction-guided synthetic receptor engineering.

Socio‐economic strategy for settlement of refugees amidst crisis: The case of Pak‐Afghan

AbstractToday, global concerns are at their peak due to the fast‐growing number of refugees. The world faces the dilemma of the refugee crisis, which needs to be addressed by the United Nations (U.N.) sustainable development goals (SDGs). Pakistan hosts about 1.4 million registered refugees in camps and urban areas. The majority consist of Afghans who departed due to severe poverty, conflict, and security concerns. These refugees bring socio‐economic distress to the host country. In order to comprehend the whole crisis, this study primarily focuses on three areas: the unstable political and security situation in the country of origin, the refugee concerns and their implications. This research is unique throughout the literature as it applies the interpretive structural modelling (ISM) tool to view the refugee crisis holistically. Results from ISM show that the ‘Political situation of Afghanistan’ is the most influential factor with very high driving power. Afterward, 10 resulting factors were opted from the ISM model, which demonstrate the implications on the host country. The relative importance index (RII) approach was used to rank the resultant factors. Ranking from RII depicts a significantly different impact of refugees while residing in urban areas. Using the Prisoner's dilemma of cooperative game theory (GT), this study concludes by recommending refugees to get registered and the host country to settle them in the local population. The use of cooperative GT with the nexus of ISM and RII is also exceptional and will be helpful for researchers and policymakers to resolve any conflict.

Discovery and characterization of noncanonical E2-conjugating enzymes.

E2-conjugating enzymes (E2s) play a central role in the enzymatic cascade that leads to the attachment of ubiquitin to a substrate. This process, termed ubiquitylation, is required to maintain cellular homeostasis and affects almost all cellular process. By interacting with multiple E3 ligases, E2s dictate the ubiquitylation landscape within the cell. Since its discovery, ubiquitylation has been regarded as a posttranslational modification that specifically targets lysine side chains (canonical ubiquitylation). We used Matrix-Assisted Laser Desorption/Ionization-Time Of Flight Mass Spectrometry to identify and characterize a family of E2s that are instead able to conjugate ubiquitin to serine and/or threonine. We used structural modeling and prediction tools to identify the key activity determinants that these E2s use to interact with ubiquitin as well as their substrates. Our results unveil the missing E2s necessary for noncanonical ubiquitylation, underscoring the adaptability and versatility of ubiquitin modifications.

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques.

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

Systemic effects of hypophosphatasia characterization of two novel variants in the ALPL gene.

Hypophosphatasia (HPP) is an inborn metabolic error caused by mutations in the ALPL gene encoding tissue non-specific alkaline phosphatase (TNSALP) and leading to decreased alkaline phosphatase (ALP) activity. Although the main characteristic of this disease is bone involvement, it presents a great genetic and clinical variability, which makes it a systemic disease. Patients were recruited based on biochemical assessments. Diagnosis was made by measuring serum ALP and pyridoxal 5-phosphate levels and finally by Sanger sequencing of the ALPL gene from peripheral blood mononuclear cells. Characterization of the new variants was performed by transfection of the variants into HEK293T cells, where ALP activity and cellular localization were measured by flow cytometry. The dominant negative effect was analyzed by co-transfection of each variant with the wild-type gene, measuring ALP activity and analyzing cellular localization by flow cytometry. Two previously undescribed variants were found in the ALPL gene: leucine 6 to serine missense mutation (c.17T>C, L6S) affecting the signal peptide and threonine 167 deletion (c.498_500delCAC, T167del) affecting the vicinity of the active site. These mutations lead mainly to non-pathognomonic symptoms of HPP. Structural prediction and modeling tools indicated the affected residues as critical residues with important roles in protein structure and function. In vitro results demonstrated low TNSALP activity and a dominant negative effect in both mutations. The results of the characterization of these variants suggest that the pleiotropic role of TNSALP could be involved in the systemic effects observed in these patients highlighting digestive and autoimmune disorders associated with TNSALP dysfunction. The two new mutations have been classified as pathogenic. At the clinical level, this study suggests that both mutations not only lead to pathognomonic symptoms of the disease, but may also play a role at the systemic level.

Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling.

The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling. We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.

Recent Advances in Biomolecular Structure Modeling and Validation using Deep Learning

Since its appearance in 2021, Alphafold2, an accurate protein structure prediction method, has quickly been adopted and substantially impacted biology research, particularly in the field of structural biology. In this review, we discuss recent advancements in the development of computational structural modeling tools related to Alphafold2. Additionally, we discuss deep learning-based structure modeling methods for cryo-electron microscopy.

THE INFLUENCE OF INCENTIVES AND COMPENSATION ON THE EMPLOYEE PERFORMANCE OF THE EDUCATION AGENCY OF NAGAN RAYA DISTRICT: THE ROLE OF WORK MOTIVATION AS A MEDIATING VARIABLE

The aim of the study was to analyze the effect of incentives and compensation on employee performance of the education agency of Nagan Raya District. Work motivation is used as a mediating variable in the functional relationship between these variables. The research was conducted on all employees of the agency of 50 persons. Data collection used a questionnaire and then the data was analyzed using statistical tools for structural equation modeling (SEM) which was then operationalized through the Smart-PLS software. The research study pointed out that incentives and compensation have a positive and significant effect on employee performance. Likewise, work motivation also significantly improves employee performance. The existence of work motivation as an intermediary variable mediates the effect of incentives and compensation on employee performance. The mediation effect is full mediation.

Strengthening the Economic Interaction of Information and Communication Technologies in The Innovative Design of Activities of Extracurricular Educational Institutions Using Electronic Educational Resources

The article is devoted to the issues of forming the digital competence of pedagogical workers of educational institutions within the framework of their own professional development program at the place of work. It is noted that the issues of designing and developing electronic educational resources (EER) using modern information and communication technologies (ICT) are actualized in the context of the digitalization of education, the activation of the use of mixed and distance learning technologies in the quarantine conditions of work of educational institutions. The article considers the main approaches to determining the content of the digital competence of teachers; e-learning; the main types of EOR, which form the open educational environment of OZ. ICT tools and methods for building a model of design, creation and use of EOR are defined. Improving the qualifications of teachers within the university is considered as corporate training, therefore, for its implementation, it is proposed to apply the concept of knowledge management by creating a repository of digital educational content. Among the modern methods of EOR design, the CASE technologies, which are based on the use of structural analysis and modeling tools, are considered the most effective. The IDEF0 model was chosen for the functional modeling of the processes of creating a computer-oriented EOR design environment. The description of the basic model "Designing EOR for an open educational environment" is detailed to the required level with the help of decomposition diagrams. The specificity of the EOR design model for an open educational environment is reflected by the functions of researching the current state of the educational environment, developing software modules of the knowledge management system, creating EOR, as well as the actual educational process and professional development. Since the project on the introduction of ICT in health care is synchronized with the process of improving the qualifications of teachers through their involvement in the development and implementation of a certain type of EOR, the proposed model allows the further use of EOR in the educational process. At Rauf Ablyazov East European University (Cherkasy), a project was developed according to the PMBoK standard, the main product of which is an LMS system integrated into the educational process of the university along with specially developed modules, as well as EOR created on its basis.

Epitope Mapping of Pathogenic Autoantigens on Sjögren’s Syndrome-Susceptible Human Leukocyte Antigens Using In Silico Techniques

Sjögren’s syndrome (SjS) is characterized by lymphocytic infiltration and the dysfunction of the salivary and lacrimal glands. The autoimmune response is driven by the effector T cells and their cytokines. The activation of the effector helper T cells is mediated by autoantigen presentation by human leukocyte antigen (HLA) class II molecules of antigen-presenting cells. Studies using familial aggregation, animal models, and genome-wide association demonstrate a significant genetic correlation between specific risk HLAs and SjS. One of the key HLA alleles is HLA-DRB1*0301; it is one of the most influential associations with primary SjS, having the highest odds ratio and occurrence across different ethnic groups. The specific autoantigens attributed to SjS remain elusive, especially the specific antigenic epitopes presented by HLA-DRB1*0301. This study applied a high throughput in silico mapping technique to identify antigenic epitopes of known SjS autoantigens presented by high-risk HLAs. Furthermore, we identified specific binding HLA-DRB1*0301 epitopes using structural modeling tools such as Immune Epitope Database and Analysis Resource IEDB, AutoDock Vina, and COOT. By deciphering the critical epitopes of autoantigens presented by HLA-DRB1*0301, we gain a better understanding of the origin of the antigens, determine the T cell receptor function, learn the mechanism of disease progression, and develop therapeutic applications.

Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins.

Oomycete and fungal interactions with plants can be neutral, symbiotic or pathogenic with different impact on plant health and fitness. Both fungi and oomycetes can generate so-called effector proteins in order to successfully colonize the host plant. These proteins modify stress pathways, developmental processes and the innate immune system to the microbes’ benefit, with a very different outcome for the plant. Investigating the biological and functional roles of effectors during plant–microbe interactions are accessible through bioinformatics and experimental approaches. The next generation protein modeling software RoseTTafold and AlphaFold2 have made significant progress in defining the 3D-structure of proteins by utilizing novel machine-learning algorithms using amino acid sequences as their only input. As these two methods rely on super computers, Google Colabfold alternatives have received significant attention, making the approaches more accessible to users. Here, we focus on current structural biology, sequence motif and domain knowledge of effector proteins from filamentous microbes and discuss the broader use of novel modelling strategies, namely AlphaFold2 and RoseTTafold, in the field of effector biology. Finally, we compare the original programs and their Colab versions to assess current strengths, ease of access, limitations and future applications.

GRP78: A possible relationship of COVID-19 and the mucormycosis; in silico perspective

Mucormycosis is a severe fungal infection reported in many cancer survivors, diabetic and immune-suppressed patients during organ transplants. A vast spark in the reported COVID-19 cases is noticed in India during the second wave in May 2021, when Mucormycosis is declared an epidemic. Despite being a rare disease, the mortality rate associated with Mucormycosis is more than 40%. Spore coat proteins (CotH) are essential proteins in many pathogenic bacteria and fungi. CotH3 was reported as the vital protein required for fungal virulence in Mucormycosis. We previously reported the involvement of the host cell-surface receptor GRP78 in SARS-CoV-2 spike recognition. Additionally, GRP78 is known to be the virulence factor during Mucormycosis. Using state-of-the-art structural bioinformatics and molecular modeling tools, we predicted the GRP78 binding site to the Rhizopus delemar CotH3 protein. Our findings pave the way toward rationally designing small molecule inhibitors targeting the GRP78 and its counter proteins in both pathogenic viral (SARS-CoV-2 spike) and fungal (R. delemar CotH3) diseases.

Extended Reality in Quantity Surveying

Purpose – It is possible for cost professionals to prepare an informed and compendious cost plan by identifying all the factors that cause cost overruns, variations, safety hazards and others without having a significant prior experience. The implementation of Extended Reality can address this phenomenon. The paper aims to introduce the concept of Extended Reality in the field of quantity surveying by exploring its untapped potential and also looks to identify critical barriers in implementing this technology.Methodology/approach –A detailed review of literature study produced eight critical factors acting as barriers in successful implementation. With the suggestions from the industry professionals, the inter-relationship among these factors were established and prioritised using Interpretative Structural Modelling (ISM) tool. Further, these factors were categorised using MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis.Findings – This study identifies, Lack of expertise and Lack of suitable software as the key driving factors in successful implementation and all the remaining factors are directly or indirectly influenced by them.Research limitations –The sample size considered in building the ISM network is limited to the Indian construction industry. The disadvantages of Extended Reality have not been covered in the study. There may be several negative repercussions to human health due to this technology.Practical implications – This study can be used by industry professionals in understanding how advance technology like this can overcome many challenges pertinent to cost planning and estimation.Originality/value – This study stands out among the few research topics which contribute to reducing the knowledge gap among the cost professionals irrespective of their experience.

(Invited) Digital Twin-Driven Battery Modeling: Understanding the Physical and Electrochemical Behavior during Fast-Charging

Long charging time is one of major inconveniences in choosing electric vehicles (EVs). In order to solve it, shortening the charging time within fifteen minutes or less is set to satisfy EV customers. However, without deep understanding both electronic and ionic behaviors within the battery, to analyze and predict the physical and electrochemical behaviors must be limited. Furthermore, the unbalance between electron and ion flows becomes more harsh as the charging rate increases. Thus, building a virtual 3D structural and electrochemical model and simulating it must be a powerful tool to look into the inside the battery during fast charging. In this work, a new digital twin battery technology, combining 3D structural and electrochemical modeling tools, is introduced and some case studies are shown to suggest how to improve the fast charging characteristics as a point of material properties and cell designs as well. Especially, electronic and ionic current flows, surface overpotential, local state-of-charge, etc are estimated and compared systematically.

Analytical management tools of industrial systems strategic development

The paper studies analytical-applied and structural aspects of strategic management of industrial systems development on the bases of assessment and analysis of the potential of their development, the formation and implementation of strategic alternatives in the conditions of external and internal environmental threats. We propose to consider the analytical-applied and structural modeling tool as a set of scenario models for formation and implementation of business entities development strategies.

Whole-Exome Sequencing of Discordant Monozygotic Twin Families for Identification of Candidate Genes for Microtia-Atresia.

ObjectiveWe used data from twins and their families to probe the genetic factors contributing to microtia-atresia, in particular, early post-twinning variations that potentially account for the discordant phenotypes of monozygotic twin pairs.MethodsSix families of monozygotic twins discordant for congenital microtia-atresia were recruited for study. The six patients shared a consistent clinical phenotype of unilateral microtia-atresia. Whole-exome sequencing (WES) was performed for all six twin pairs and their parents. Family segregation and multiple bioinformatics methods were applied to identify suspicious mutations in all families. Recurring mutations commonly detected in at least two families were highlighted. All variants were validated via Sanger sequencing. Gene Ontology (GO) analysis was performed to identify candidate gene sets and related pathways. Copy number variation (CNV), linkage analysis, association analysis and machine learning methods were additionally applied to isolate candidate mutations, and comparative genomics and structural modeling tools used to evaluate their potential roles in onset of microtia-atresia.ResultsOur analyses revealed 61 genes with suspected mutations associated with microtia-atresia. Five (HOXA4, MUC6, CHST15, TBX10, and AMER1) contained 7 de novo mutations that appeared in at least two families, which have been previously reported as pathogenic for other diseases. Among these, HOXA4 (c.920A>C, p.H307P) was determined as the most likely pathogenic variant for microtia-atresia. GO analysis revealed four gene sets involving 11 pathways potentially related to underlying pathogenesis of the disease. CNVs in three genes (UGT2B17, OVOS, and KATNAL2) were detected in at least two families. Linkage analysis disclosed 13 extra markers for the disease, of which two (FGFR1 and EYA1) were validated via machine learning analysis as plausible candidate genes for the disease.ConclusionBased on comprehensive genetic and bioinformatic analyses of WES data from six families of discordant monozygotic twins with microtia-atresia, we identified multiple candidate genes that may function in post-twinning onset of the disease. The collective findings provide novel insights into the pathogenesis of congenital microtia-atresia.

Puromycin, a selective inhibitor of PSA acts as a substrate for other M1 family aminopeptidases: Biochemical and structural basis

Puromycin sensitive aminopeptidase (PSA or NPEPPS) is a M1 class aminopeptidase is selectively inhibited by the natural product puromycin, an aminonucleoside antibiotic produced by the bacterium Streptomyces alboniger. The molecular basis for this selective inhibition has not been understood well. Here, we report the basis for selectivity of puromycin using biochemical, structural and molecular modeling tools on four different M1 family enzymes including human PSA. Except for PSA, the other three enzymes were not inhibited. Instead, the peptide bond in the puromycin is hydrolyzed to O-methyl-L-tyrosine (OMT) and puromycin aminonucleoside (PAN). Neither of the hydrolyzed products, individually or together inhibit any of the four enzymes. Crystal structure of ePepN using crystals that are incubated with puromycin contained the hydrolyzed products instead of intact puromycin. On the other hand, intact puromycin molecule was observed in the crystal structure of the inactive mutant ePepN (E298A)-puromycin complex. Surprisingly, puromycin does not enter the active site of the mutant enzyme but binds near the entrance. Comparison of puromycin binding region in ePepN mutant enzyme and molecular modeling studies suggest that PSA might be inhibited by similar mode of binding there by blocking the entrance of the active site.

Numerical investigation of response-conditioning wave techniques for short-term rare combined loading scenarios

Response-conditioning wave techniques are a rational way to link wave excitation environments with return-period extreme loading responses. By retaining the wave excitation which leads to a design response, these techniques can also define extreme combined loading scenarios. For complex or novel hull forms, combined loading may be relevant for evaluating structural reliability or adequacy. But using combined loading scenarios as inputs to high-fidelity structural or dynamic modeling tools implies that such load scenarios are realistic for the defined return-period. This paper investigates three response-conditioning wave techniques: a modified Equivalent Design Wave method, a modified Conditioned Random Response Wave method, and the Design Loads Generator, to evaluate how well they reproduce combined loading statistics for a 1000-hr return-period as compared to stochastic brute-force simulations. The investigation is carried out for extreme combined loading scenarios on a 110 m trimaran hull. The Design Loads Generator produces the most realistic extreme combined loading statistics as compared to the brute-force approach with a significant reduction in computation time based on combined load conditional probability density functions, cumulative density functions, and individual stochastic load vectors.

Identification of Critical Factors and Their Interrelationships to Design Agile Supply Chain: Special Focus to Oil and Gas Industries

This research identifies the critical factors that drive agile supply chain management, especially to oil and gas industries. An extensive literature review and questionnaire survey was conducted within the oil and gas industries supply chain to identify the critical factors. Besides, several brainstorming sessions were held with the experts in the oil and gas industries supply chain to know the contextual relationship between the identified factors. Furthermore, with the help of a total interpretive structural modeling tool, a digraph was developed to analyze the relationships between the identified factors. From the analysis, out of the twelve identified factors, top management commitment, strategic alignment, competency of management and integration of information technology were found to be the main drivers to offer an agile supply chain. On the other hand, factors such as government regulations, transportation and logistics flexibility and production planning fell under the category of dependent factors.