Protein Structure Prediction: Conventional and Deep Learning Perspectives

Using Attention-UNet Models to Predict Protein Contact Maps

Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue-residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Quantum chemical "Aufbau" principles: how to estimate the shape of highly flexible (bio-)polymers? A recursively extendable "chemion picture" of Euler-Hückel-type

An outline is given of how to split the n-dimensional space of torsion angles occurring in flexible (bio-)polymers (like alkanes, nucleic acids, or proteins, for instance) into n one-dimensional potential curves. Forthcoming applications will focus on the "protein folding problem," beginning with polyglycine. CONTEXT: In accordance with Euler's topology rules, molecules are considered to be composed of "vertices" (atoms, ligands, bonding sites, functional groups, and bigger fragments). Following Hückel, each vertex is represented by only one basis function. Starting from the "monofocal" hydrids CH[Formula: see text], NH[Formula: see text], OH[Formula: see text], FH, and SiH[Formula: see text], PH[Formula: see text], SH[Formula: see text], ClH as anchor units, "chemionic" Hamiltonians (of individual "chemion ensembles" and proportional nuclear charges) are constructed recursively, together with an appropriate basis set for the first five (normal) alkanes and some related oligomers like primary alcohols, alkyl amines, and alkyl chlorides. METHODS: Standard methods ("Restricted Hartree-Fock RHF" and "Full Configuration Interaction FCI") are used to solve the various stationary Schrödinger equations. Two software packages are indispensable: "SMILES" for integral evaluations over Slater-type orbitals (STO), and "Numerical Recipes" for matrix diagonalizations and inversions. While managing with only two-center repulsion integrals, "implicit multi-center integrations" lead us to the non-empirical fundament of Hoffmann's "Extended-Hückel Theory."

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Combined Computational-Biochemical Approach Offers an Accelerated Path to Membrane Protein Solubilization

Membrane proteins are difficult to isolate and purify due to their dependence on the surrounding lipid membrane for structural stability. Detergents are often used to solubilize these proteins, with this approach requiring a careful balance between protein solubilization and denaturation. Determining which detergent is most appropriate for a given protein has largely been done empirically through screening, which requires large amounts of membrane protein and associated resources. Here, we describe an alternative to conventional detergent screening using a computational modeling approach to identify the most likely candidate detergents for solubilizing a protein of interest. We demonstrate our approach using ghrelin O-acyltransferase (GOAT), a member of the membrane-bound O-acyltransferase family of integral membrane enzymes that has not been solubilized or purified in active form. A computationally derived GOAT structural model provides the only structural information required for this approach. Using computational analysis of detergent ability to penetrate phospholipid bilayers and stabilize the GOAT structure, a panel of common detergents were rank-ordered for their proposed ability to solubilize GOAT. The simulations were performed at all-atom resolution for a combined simulation time of 24 μs. Independently, we biologically screened these detergents for their solubilization of fluorescently tagged GOAT constructs. We found computational prediction of protein structural stabilization was the better predictor of detergent solubilization ability, but neither approach was effective for predicting detergents that would support GOAT enzymatic function. The current rapid expansion of membrane protein computational models lacking experimental structural information and our computational detergent screening approach can greatly improve the efficiency of membrane protein detergent solubilization, supporting downstream functional and structural studies.

A Proposal for a Consolidated Structural Model of the CagY Protein of Helicobacter pylori

CagY is the largest and most complex protein from Helicobacter pylori's (Hp) type IV secretion system (T4SS), playing a critical role in the modulation of gastric inflammation and risk for gastric cancer. CagY spans from the inner to the outer membrane, forming a channel through which Hp molecules are injected into human gastric cells. Yet, a tridimensional structure has been reported for only short segments of the protein. This intricate protein was modeled using different approaches, including homology modeling, ab initio, and deep learning techniques. The challengingly long middle repeat region (MRR) was modeled using deep learning and optimized using equilibrium molecular dynamics. The previously modeled segments were assembled into a 1595 aa chain and a 14-chain CagY multimer structure was assembled by structural alignment. The final structure correlated with published structures and allowed to show how the multimer may form the T4SS channel through which CagA and other molecules are translocated to gastric cells. The model confirmed that MRR, the most polymorphic and complex region of CagY, presents numerous cysteine residues forming disulfide bonds that stabilize the protein and suggest this domain may function as a contractile region playing an essential role in the modulating activity of CagY on tissue inflammation.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Computational insight into stability-enhanced systems of anthocyanin with protein/peptide

Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of plants native to South and Central America. However, these pigments are unstable under conditions of varying pH, heat, etc., which limits their potential applications. One method for preserving the stability of anthocyanins is through encapsulation using proteins or peptides. Nevertheless, the complex and diverse structure of these molecules, as well as the limitation of experimental technologies, have hindered a comprehensive understanding of the encapsulation processes and the mechanisms by which stability is enhanced. To address these challenges, computational methods, such as molecular docking and molecular dynamics simulation have been used to study the binding affinity and dynamics of interactions between proteins/peptides and anthocyanins. This review summarizes the mechanisms of interaction between these systems, based on computational approaches, and highlights the role of proteins and peptides in the stability enhancement of anthocyanins. It also discusses the current limitations of these methods and suggests possible solutions.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Personal Precise Force Field for Intrinsically Disordered and Ordered Proteins Based on Deep Learning

Intrinsically disordered proteins (IDPs) are proteins without a fixed three-dimensional (3D) structure under physiological conditions and are associated with Parkinson's disease, Alzheimer's disease, cancer, cardiovascular disease, amyloidosis, diabetes, and other diseases. Experimental methods can hardly capture the ensemble of diverse conformations for IDPs. Molecular dynamics (MD) simulations can sample continuous conformations that might provide a valuable complement to experimental data. However, the accuracy of MD simulations depends on the quality of force field. In particular, the evolutionary conservation and coevolution of IDPs introduce that current force fields could not precisely reproduce the conformation of IDPs. In order to improve the performance of force field, deep learning and reweighting methods were used to automatically generate personal force field parameters for intrinsically disordered and ordered proteins. At first, the deep learning method predicted more accuracy φ/ψ dihedral of residue than the previous method. Then, reweighting optimized the personal force field parameters for each residue. Finally, typical representative systems such as IDPs, structure protein, and fast-folding protein were used to evaluate this force field. The results indicate that two personal force field parameters (named PPFF1 and PPFF1_af2) could better reproduce the experimental observables than ff03CMAP force field. In summary, this strategy will provide feasibility for the development of precise personal force fields.

Challenges in antibody structure prediction

Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.

Cloning and identification of the CTLA-4IgV gene and functional application of vaccine in Xinjiang sheep

Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is an important surface molecule of activated T cells that has a strong affinity with the B7 molecule on the surface of antigen-presenting cells. Among these molecules, the CTLA-4 extracellular region (CTLA-4 IgV) may be used as a novel immune adjuvant molecule for delivering antigens and inducing strong humoral and cellular immune responses. In this study, bioinformatics analysis was performed to determine and clone the extracellular region of Xinjiang sheep CTLA-4 (NM_001009214). The CTLA-4 IgV gene was amplified and ligated into the pMD19-T vector, and the positive bacteria were screened by blue-white spots for sequencing and comparison. The correctly sequenced CTLA-4 IgV was digested and then ligated into the prokaryotic expression vector pET-30a(+). The plasmid pET30a–CTLA-4 IgV was constructed to induce the expression of the recombinant protein CTLA-4 IgV. Thereafter, CTLA-4 IgV was identified. Clustal X multiple sequence alignment revealed that the protein sequence of Xinjiang sheep CTLA-4 IgV was different from that of the known CTLA-4 extracellular region. The 3D protein structure of Xinjiang sheep CTLA-4 IgV was constructed via the bioinformatics method. Subsequently, molecular docking between the Xinjiang sheep CTLA-4 IgV protein and the B7 molecule was conducted. Results revealed multiple binding sites in the extracellular region of Xinjiang sheep CTLA-4, and two multiple interactions ensured stable binding after docking. The functionality of the Xinjiang sheep CTLA-4 IgV protein was further verified by fusing the CTLA-4 extracellular V region with EgG1Y162, a protective protein from Echinococcus granulosa, and the purified recombinant protein CTLA-4 IgV–EgG1Y162 was expressed with the mouse bone marrow-derived. The addition of the Xinjiang sheep CTLA-4 IgV protein at the amino terminus promoted the binding of EgG1Y162 to dendritic cells (DCs) and increased the maturation rate of these cells, further indicating that the protein could effectively improve the antigen presentation ability of DCs. The CTLA-4 extracellular domain protein of Xinjiang sheep is unique and has the potential to promote the presentation of the fusion protein by DCs as an adjuvant. The cloning and expression of this gene provide new measures and ideas for the preparation of the Xinjiang sheep vaccine to prevent zoonotic diseases.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Protein secondary structure assignment using residual networks

Proteins are constructed from amino acid sequences. Their structural classifications include primary, secondary, tertiary, and quaternary, with tertiary and quaternary structures influencing protein function. Because a protein's structure is inextricably connected to its biological function, machine learning algorithms that can better anticipate the structures have the potential to lead to new scientific discoveries in human health and improve our capacity to develop new treatments. Protein secondary structure assignment enriches the structural and functional understanding of proteins. It helps in protein structure comparison and classification studies, besides facilitating secondary and tertiary structure prediction systems. Several secondary structure assignment methods have been developed since the 1980s, most of which are based on hydrogen bond analysis and atomic coordinate features. However, the assignment process becomes complex when protein data includes missing atoms. Deep neural networks are often referred to as universal function approximators because they can approximate any function to produce the desired output when properly designed and trained. Optimised deep learning architectures have already proven their ability to increase performance in a wide range of problems. Recently, the ResNet architecture has garnered significant interest due to its applicability in various areas, including image classification and protein contact map prediction. The proposed model, which is based on the ResNet architecture, assigns secondary structures using Cα atom coordinates. The model achieved an accuracy of 94% when evaluated against the benchmark and independent test sets. The findings encourage the development of new deep learning-based methods that are more generalised across various protein learning tasks. Furthermore, it allows computational biologists to delve deeper into integrating these techniques with experimental methods. The model codes are available at: https://github.com/jisnava/ResNet_for_Structure_Assignments/ .

The Possible Mechanism of Amyloid Transformation Based on the Geometrical Parameters of Early-Stage Intermediate in Silico Model for Protein Folding

The specificity of the available experimentally determined structures of amyloid forms is expressed primarily by the two- and not three-dimensional forms of a single polypeptide chain. Such a flat structure is possible due to the β structure, which occurs predominantly. The stabilization of the fibril in this structure is achieved due to the presence of the numerous hydrogen bonds between the adjacent chains. Together with the different forms of twists created by the single R- or L-handed α-helices, they form the hydrogen bond network. The specificity of the arrangement of these hydrogen bonds lies in their joint orientation in a system perpendicular to the plane formed by the chain and parallel to the fibril axis. The present work proposes the possible mechanism for obtaining such a structure based on the geometric characterization of the polypeptide chain constituting the basis of our early intermediate model for protein folding introduced formerly. This model, being the conformational subspace of Ramachandran plot (the ellipse path), was developed on the basis of the backbone conformation, with the side-chain interactions excluded. Our proposal is also based on the results from molecular dynamics available in the literature leading to the unfolding of α-helical sections, resulting in the β-structural forms. Both techniques used provide a similar suggestion in a search for a mechanism of conformational changes leading to a formation of the amyloid form. The potential mechanism of amyloid transformation is presented here using the fragment of the transthyretin as well as amyloid Aβ.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

RNA Cleavage Properties of Nucleobase-Specific RNase MC1 and Cusativin Are Determined by the Dinucleotide-Binding Interactions in the Enzyme-Active Site

Knowledge of the cleavage specificity of ribonucleases is critical for their application in RNA modification mapping or RNA-protein binding studies. Here, we detail the cleavage specificity and efficiency of ribonuclease MC1 and cusativin using a customized RNA sequence that contained all dinucleotide combinations and homopolymer sequences. The sequencing of the oligonucleotide digestion products by a semi-quantitative liquid chromatography coupled with mass spectrometry (LC-MS) analysis documented as little as 0.5-1% cleavage levels for a given dinucleotide sequence combination. While RNase MC1 efficiently cleaved the [A/U/C]pU dinucleotide bond, no cleavage was observed for the GpU bond. Similarly, cusativin efficiently cleaved Cp[U/A/G] dinucleotide combinations along with UpA and [A/U]pU, suggesting a broader specificity of dinucleotide preferences. The molecular interactions between the substrate and active site as determined by the dinucleotide docking studies of protein models offered additional evidence and support for the observed substrate specificity. Targeted alteration of the key amino acid residues in the nucleotide-binding site confirms the utility of this in silico approach for the identification of key interactions. Taken together, the use of bioanalytical and computational approaches, involving LC-MS and ligand docking of tertiary structural models, can form a powerful combination to help explain the RNA cleavage behavior of RNases.

Tools for protein structure prediction and for molecular docking applied to enzyme active site analysis: A case study using a BAHD hydroxycinnamoyltransferase

Elucidating the structure of an enzyme and how substrates bind to the active site is an important step for understanding its reaction mechanism and function. Nevertheless, the methods available to obtain three-dimensional structures of proteins, such as x-ray crystallography and NMR, can be expensive and time-consuming. Considering this, an alternative is using structural bioinformatic tools to predict the tertiary structure of a protein from its primary sequence, followed by molecular docking of one or more substrates into the enzyme structure model. In the past few years, significant advances have been made in these computational tools, which can give useful information about the active site and enzyme-substrate interactions before the structure can be resolved using physical methods. Here, using common bean (Phaseolus vulgaris) hydroxycinnamoyl-coenzyme A:tetrahydroxyhexanedioic acid hydroxycinnamoyltransferase (HHHT) as an example, we describe methods and workflows for protein structure prediction and molecular docking that can be performed on a personal computer using only open-source tools.

Molecular Modeling and Bioinformatics Analysis of Drug-Receptor Interactions in the System Formed by Glargine, Its Metabolite M1, the Insulin Receptor, and the IGF1 Receptor

Introduction

Insulin and insulin-like growth factor type 1 (IGF1) regulate multiple physiological functions by acting on the insulin receptor (IR) and insulin-like growth factor type 1 receptor (IGF1R). The insulin analog glargine differs from insulin in three residues (Gly^A21, Arg^B31, Arg^B32), and it is converted to metabolite M1 (lacks residues Arg^B31 and Arg^B32) by in vivo processing. It is known that activation of these receptors modulates pathways related to metabolism, cell division, and growth. Though, the structures and structural basis of the glargine interaction with these receptors are not known.

Aim

To generate predictive structural models, and to analyze the drug/receptor interactions in the system formed by glargine, its metabolite M1, IR, and IGF1R by using bioinformatics tools.

Methods

Ligand/receptor models were built by homology modeling using SWISSMODEL, and surface interactions were analyzed using Discovery Studio® Visualizer. Target and hetero target sequences and appropriate template structures were used for modeling.

Results

Our glargine/IR and metabolite M1/IR models showed an overall symmetric T-shaped conformation and full occupancy with four ligand molecules. The glargine/IR model revealed that the glargine residues Arg^B31 and Arg^B32 fit in a hydrophilic region formed by the α-chain C-terminal helix (αCT) and the cysteine-rich region (CR) domain of this receptor, close to the CR residues Arg270-Arg271-Gln272 and αCT residue Arg717. Regarding IGF1R, homologous ligand/receptor models were further built assuming that the receptor is in a symmetrical T-shaped conformation and is fully occupied with four ligand molecules, similar to what we described for IR. Our glargine/IGF1R model showed the interaction of the glargine residues Arg^B31 and Arg^B32 with Glu264 and Glu305 in the CR domain of IGF1R.

Conclusion

Using bioinformatics tools and predictive modeling, our study provides a better understanding of the glargine/receptor interactions.