Multiscale Modeling of Intra- and Inter-Molecular Communications Through Protein Structures

Protein-ligand interactions in crowded cellular environments play a crucial role in biological functions. The crowded environment can perturb the overall protein structure and local conformation, thereby influencing the binding pathway of protein-ligand reactions within the cellular milieu. Therefore, a detailed understanding of the local conformation is crucial for elucidating the intricacies of protein-ligand interactions in crowded cellular environments. In this study, we investigated the feasibility of induced circular dichroism (ICD) using 8-anilinonaphthalene-1-sulfonic acid (ANS) for local conformational analysis at the binding site in a crowding environment. Bovine serum albumin (BSA) concentration-dependent measurements were performed to assess the feasibility of ANS-ICD for analyzing protein interior binding sites. The results showed distinct changes in the ANS-ICD spectra of BSA solutions, indicating their potential for analyzing the internal conformation of proteins. Moreover, temperature-dependent measurements were performed in dilute and crowding environments, revealing distinct denaturation pathways of BSA binding sites. Principal component analysis of ANS-ICD spectral changes revealed lower temperature pre-denaturation in the crowded solution than that in the diluted solution, suggesting destabilization of binding sites owing to self-crowding repulsive interactions. The established ANS-ICD method can provide valuable conformational insights into protein-ligand interactions in crowded cellular environments.

Local variations in protein structure in the human eye lens: a Raman microspectroscopic study

Role of calcium binding in protein structural changes and phospholipid–protein interactions studied by capillary isoelectric focusing with whole column imaging detection

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Protein Proteins Interactions (PPIs) is a process of interacting protein with proteins in order to produce some organic procedures. To make better understanding of recognizing protein work it is necessary to create high throughput strategies for distinguishing PPI. Protein protein interaction is used for different cells also it defines the 3D structure of proteins. It facilitates different cell capacities resulting from 3D structure of cell that is three dimensional structures of protein protein complexes interactions and it binds affinity information together. The purpose of PPIs is to make interactions between bacterial, viral, and parasitic pathogens of human host's harbors which have great medicinal making potential in order to reduce the causes of dieses that occur due to the PPIs in humans. PPIs are used to discover target specific disease with related interfaces that underlines human interaction network. The structure of ligand binding proteins has to face several challenges including protein sampling of the huge possible orientations for ligand, the protein pocket, sequence space of large data and estimating the binding free energies accurately during the design process. Computational methods are used for successful ligand binding protein design their pros and cons and the potential future directions of the field are discussed. In this paper we have analyzed different methods and techniques of PPIs identification, management, interactions and bindings also to gather different analysis and results based on big databases. In future, we will use Spark's distributed machine Learning library for PPIs prediction, data modeling and machine learning.

Structural characterization of amyloid (A)β peptides implicated in Alzheimer's disease is a challenging problem due to their intrinsically disordered nature and their high propensity for aggregation. Only limited information is currently available from experiments on conformational properties and aggregation pathways of the peptides in cellular environments. In silico modeling complements experimental information, providing atomistic insight into structure and dynamics of different Aβ species. All-atom explicit solvent molecular dynamics (MD) simulations with a properly selected force field can deliver reliable structural and dynamic information. In the case of intrinsically disordered Aβ peptides, enhanced sampling simulations beyond the nanosecond time scale are required to obtain statistically meaningful results even for simple solvent conditions. To overcome the challenges of conformational sampling in crowded cellular environments, alternative approaches have to be used, including postprocessing of MD data. In this study, we employ the statistical-mechanical, three-dimensional reference interaction site model with the Kovalenko-Hirata closure integral equation molecular theory of solvation to describe solvent composition effects on the conformational equilibrium in a structural ensemble of the Aβ42 (covering residues 1-42) monomer based on a statistical reweighting technique. The methodology enables a computationally efficient prediction on how different factors in the cellular environment, such as solvent composition, nonpolar solvation, and macromolecular crowding, affect the structural properties of the monomer. Similarities have been identified between changes in the structural ensemble caused by nonpolar solvation and crowded environments modeled by ionic solution with large negative ions. In particular, both solvent conditions reduce the random coil content and enhance the helical structure content of the monomer. In contrast to the previous studies, which reported increased α-helical content of peptides in crowded environments, this work attributes these structural features to the difference in solvent exposure of hydrophilic residues of the monomer for different secondary structure elements, rather than to (entropic) excluded volume effects.

Protein three-dimensional structure dynamically changes in solution depending on the presence of ligands and interacting proteins. Methods for detecting these changes in protein conformation include ‘protein footprinting,’ using mass spectrometry. We describe herein a new technique, PLIMB (Plasma Induced Modification of Biomolecules), that generates µs bursts of hydroxyl radicals from water, to measure changes in protein structure via altered solvent accessibility of amino acid side chains. PLIMB was first benchmarked with model compounds, and then applied to a biological problem, i.e., ligand (EGF) induced changes in the conformation of the external (ecto) domain of Epidermal Growth Factor Receptor (EGFR). Regions in which oxidation decreased upon adding EGF fall along the dimerization interface, consistent with models derived from crystal structures. These results demonstrate that plasma-generated hydroxyl radicals from water can be used to map protein conformational changes, and provide a readily accessible means of studying protein structure in solution.

CellBio-X: celebrating the interface between Cell Biology and other disciplines

Inter-residue interactions play an essential role in driving protein folding, and analysis of these interactions increases our understanding of protein folding and stability and facilitates the development of tools for protein structure and function prediction. In this work, we systematically characterized the change of inter-residue interactions at various sequence separation cutoffs using two protein datasets. The first set included 100 diverse, nonredundant and high-resolution soluble protein structures, covering all four major structural classes, all-alpha, alpha/beta, alpha+beta, and all-beta; and the second set included 20 diverse, nonredundant and high-resolution membrane protein structures, representing 19 unique superfamilies. It was shown that the average number of inter-residue interactions in structures of both datasets displays the power-law behavior. Fitting parameters of the power-law function are directly related to the structural classes analyzed. These findings provided further insight into the distribution of short-, medium-, and long-range inter-residue interactions in both soluble and membrane proteins and could be used for protein structure prediction.

A model for identification of potential phase-separated proteins based on protein sequence, structure and cellular distribution

In the last decade, various coarse-grained elastic network models have been developed to study the large-scale motions of proteins and protein complexes where computer simulations using detailed all-atom models are not feasible. Among these models, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM) have been widely used. Both models have strengths and limitations. GNM can predict the relative magnitudes of the fluctuations well, but due to its isotropic assumption, it can not be applied to predict the directions of the fluctuations. In contrast, ANM adds the ability to do the latter, but it loses a significant amount of precision in the prediction of the magnitudes. In this article, we develop a generalized spring tensor model (STeM) that is able to predict well both the magnitudes and the directions of the fluctuations. STeM also outperforms ANM in explaining protein conformation changes. All of these are accomplished without sacrificing the essential features that have made ANM and GNM attractive.

Interaction of metals with prion protein: Possible role of divalent cations in the pathogenesis of prion diseases

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) causing coronavirus disease 2019 (COVID‐19) has infected 10 millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to a change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 20 nonsynonymous mutations in the SARS‐CoV‐2 genome in which incidence rates were all ≥ 1% as of September 1st, 2020, and then modeled and analyzed the mutant protein structures. The results showed that four types of mutations caused dramatic changes in protein structures (RMSD ≥ 5.0 Å), which were Q57H and G251V in open‐reading frames 3a (ORF3a), S194L, and R203K/G204R in nucleocapsid (N). Next, we found that these mutations also affected the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking mutant complexes were altered, among which the mutation Q57H was involved in both Orf3a–S and Orf3a–Orf8 protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS‐CoV‐2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on SARS‐CoV‐2 transmission, diagnosis, and treatment of COVID‐19.

Detection of altered protein conformations in living cells

Protein-based particles are very promising colloidal systems for protection and controlled release applications in the food, cosmetics and pharmaceutical sector. One technique to produce these protein colloidal particles is liquid antisolvent precipitation (LAS). Despite the simplicity and versatility of LAS, not much is known about the protein conformational changes and interactions that are at the basis of the particle formation process. In this study, steady state fluorescence experiments using intrinsic fluorophores were evaluated as a tool to unravel the dynamics of the protein nanoparticle formation. Colloidal whey protein isolate and gliadin particles were produced by LAS. Changes in particle diameter (distribution), polydispersity index and photophysical properties of intrinsic fluorophores were monitored as a function of antisolvent concentration. By combining dynamic light scattering with photophysical data, a model of the changes occurring during particle formation and disintegration could be proposed. The results suggest that particle formation and disintegration are fully reversible processes during which the main changes in protein conformation (around the fluorescent probes) occur at the same antisolvent concentrations. In principle, steady state fluorescence measurements using intrinsic probes can indeed be used to effectively report on (part of the) conformational changes for both protein systems under study.