Structural Studies Of Proteins Research Articles

The Applications of Molecular Dynamics Simulation in Studying Protein Structure and Dynamics

The Applications of Molecular Dynamics Simulation in Studying Protein Structure and Dynamics

A novel variant in the FLNB gene associated with spondylocarpotarsal synostosis syndrome.

Genetic disorders involved in skeleton system arise due to the disturbance in skeletal development, growth and homeostasis. Filamin B is an actin binding protein which is large dimeric protein which cross link actin cytoskeleton filaments into dynamic structure. A single nucleotide changes in the FLNB gene causes spondylocarpotarsal synostosis syndrome, a rare bone disorder due to which the fusion of carpels and tarsals synostosis occurred along with fused vertebrae. In the current study we investigated a family residing in north-western areas of Pakistan. The whole exome sequencing of proband was performed followed by Sanger sequencing of all family members of the subject to validate the variant segregation within the family. Bioinformatics tools were utilized to assess the pathogenicity of the variant. Whole Exome Sequencing revealed a novel variant (NM_001457: c.209C>T and p.Pro70Leu) in the FLNB gene which was homozygous missense mutation in the FLNB gene. The variant was further validated and visualized by Sanger sequencing and protein structure studies respectively as mentioned before. The findings have highlighted the importance of the molecular diagnosis in SCT (spondylocarpotarsal synostosis syndrome) for genetic risk counselling in consanguineous families.

Antibodies expand the scope of angiotensin receptor pharmacology.

G-protein-coupled receptors (GPCRs) are key regulators of human physiology and are the targets of many small-molecule research compounds and therapeutic drugs. While most of these ligands bind to their target GPCR with high affinity, selectivity is often limited at the receptor, tissue and cellular levels. Antibodies have the potential to address these limitations but their properties as GPCR ligands remain poorly characterized. Here, using protein engineering, pharmacological assays and structural studies, we develop maternally selective heavy-chain-only antibody ('nanobody') antagonists against the angiotensin II type I receptor and uncover the unusual molecular basis of their receptor antagonism. We further show that our nanobodies can simultaneously bind to angiotensin II type I receptor with specific small-molecule antagonists and demonstrate that ligand selectivity can be readily tuned. Our work illustrates that antibody fragments can exhibit rich and evolvable pharmacology, attesting to their potential as next-generation GPCR modulators.

Structural Characterization of Monoclonal Antibodies and Epitope Mapping by FFAP Footprinting.

Covalent labeling in combination with mass spectrometry is a powerful approach used in structural biology to study protein structures, interactions, and dynamics. Recently, the toolbox of covalent labeling techniques has been expanded with fast fluoroalkylation of proteins (FFAP). FFAP is a novel radical labeling method that utilizes fluoroalkyl radicals generated from hypervalent Togni reagents for targeting aromatic residues. This report further demonstrates the benefits of FFAP as a new method for structural characterization of therapeutic antibodies and interaction interfaces of antigen-antibody complexes. The results obtained from human trastuzumab and its complex with human epidermal growth factor receptor 2 (HER2) correlate well with previously published structural data and demonstrate the potential of FFAP in structural biology.

Proteins Can Withstand More Extensive Labeling while Providing Accurate Structural Information in Covalent Labeling-Mass Spectrometry.

Diethylpyrocarbonate (DEPC) covalent labeling-mass spectrometry (CL-MS) has been extensively utilized to study protein structure and interactions owing to its ease of use, commercial availability, and broad labeling of nucleophilic residues. During typical CL-MS experiments with DEPC, the extent of labeling is kept low to avoid any structural perturbations resulting from covalent modification of the protein. In this study, we demonstrate that proteins can be labeled more extensively via DEPC and still provide accurate structural information. To show this, we modeled labeling kinetics over a range of DEPC concentrations and used molecular dynamics simulations to investigate the molecular-level effects of extensive labeling on the protein structure. Our results indicate that higher extents of DEPC labeling do not significantly perturb the protein structure and can lead to improved precision, detectability of labeled peptides, and protein structural resolution. Furthermore, higher extents of labeling enable better identification of protein-ligand binding sites where lower extents of modification provide ambiguous results.

An analysis of double-quantum coherence ESR in an N-spin system: Analytical expressions and predictions.

Electron spin resonance pulsed dipolar spectroscopy (PDS) has become popular in protein 3D structure analysis. PDS studies yield distance distributions between a pair or multiple pairs of spin probes attached to protein molecules, which can be used directly in structural studies or as constraints in theoretical predictions. Double-quantum coherence (DQC) is a highly sensitive and accurate PDS technique to study protein structures in the solid state and under physiologically relevant conditions. In this work, we have derived analytical expressions for the DQC signal for a system with N-dipolar coupled spin-1/2 particles in the solid state. The expressions are integrated over the relevant spatial parameters to obtain closed form DQC signal expressions. These expressions contain the concentration-dependent "instantaneous diffusion" and the background signal. For micromolar and lower concentrations, these effects are negligible. An approximate analysis is provided for cases of finite pulses. The expressions obtained in this work should improve the analysis of DQC experimental data significantly, and the analytical approach could be extended easily to a wide range of magnetic resonance phenomena.

HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures.

Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibrant research area. In this paper, we introduce HydraProt (deep Hydration of Proteins), a novel methodology for predicting precise positions of water molecule oxygen atoms around protein structures, leveraging two interconnected deep learning architectures: a 3D U-net and a Multi-Layer Perceptron (MLP). Our approach starts by introducing a coarse voxel-based representation of the protein, which allows for rapid sampling of candidate water positions via the 3D U-net. These water positions are then assessed by embedding the water-protein relationship in the Euclidean space by means of an MLP. Finally, a postprocessing step is applied to further refine the MLP predictions. HydraProt surpasses existing state-of-the-art approaches in terms of precision and recall and has been validated on large data sets of protein structures. Notably, our method offers rapid inference runtime and should constitute the method of choice for protein structure studies and drug discovery applications. Our pretrained models, data, and the source code required to reproduce these results are accessible at https://github.com/azamanos/HydraProt.

Multilevel superposition for deciphering the conformational variability of protein ensembles.

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Proteomics and Its Current Application in Biomedical Area: Concise Review.

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Controlling the incorporation of fluorinated amino acids in human cells and its structural impact.

Fluorinated aromatic amino acids (FAAs) are promising tools when studying protein structure and dynamics by NMR spectroscopy. The incorporation FAAs in mammalian expression systems has been introduced only recently. Here, we investigate the effects of FAAs incorporation in proteins expressed in human cells, focusing on the probability of incorporation and its consequences on the 19 F NMR spectra. By combining 19 F NMR, direct MS and x-ray crystallography, we demonstrate that the probability of FAA incorporation is only a function of the FAA concentration in the expression medium and is a pure stochastic phenomenon. In contrast with the MS data, the x-ray structures of carbonic anhydrase II reveal that while the 3D structure is not affected, certain positions lack fluorine, suggesting that crystallization selectively excludes protein molecules featuring subtle conformational modifications. This study offers a predictive model of the FAA incorporation efficiency and provides a framework for controlling protein fluorination in mammalian expression systems.

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology.

The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins' functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.

Whole exome sequencing coupled with in silico functional analysis identified NID1 as a novel candidate gene causing neuro-psychiatric disorder in a Pakistani family

Intellectual disability (ID) is a neuro-developmental condition that affects a person’s cognitive ability and results in a learning defect. It affects 1–3% of the general population; however, the ratio may be expected to be more in a consanguineous population. Herein in the present study, we identified a nuclear family from Dera Ismail Khan City in Pakistan. Whole exome sequencing was performed to map the pathogenic variant. Protein structural modeling and interaction studies were carried out to validate the variant with disease association. Molecular modeling of normal and mutated proteins was performed through I-TASSER and Chimera tools, while docking and interaction analysis was carried out using Cluspro. Clinical analysis of the patient determined mild intellectual disability and gait problem. Candidate gene analysis in this family found a homozygous missense mutation NM_002508:c.C2512T (p.Arg838Cys) in the 12th exon of NID1 gene. Molecular modeling of wild-type and mutant NID1 proteins determined a significant effect on the protein’s secondary and tertiary structure. Hence, based on the exome sequence analysis, NID1 is proposed to be a strong novel candidate ID gene in this family. The genetic mapping of the present family led us to determine a novel candidate gene to be associated with intellectual disability. Linkage of additional ID families with genes would confirm its validity and strengthen our notion. Furthermore, expression studies and pathway analysis will help in exploring the biological mechanism of learning and memory.

Study of the Three-Dimensional Structure of Tryptophan Zipper Peptides through 1H NMR Chemical Shifts Calculations

Three-dimensional structures of proteins are intimately linked to their functions, therefore understanding their conformation in solution is essential. While nuclear magnetic resonance spectroscopy and X-ray crystallography are widely employed for protein structural determination, their limitations make the process challenging and expensive. Theoretical calculations of chemical shifts present a potential complement to experimental techniques, facilitating the study of protein structures. This investigation aims to assess the applicability of chemical shift calculations in analyzing three-dimensional structures of peptides, focusing on the tryptophan zipper 1 peptide as a model. Furthermore, a mutated variant of this peptide was proposed to evaluate the stability of its structural elements under sequence modifications. Through calculations, a potential structural alteration in the β-turn region of the mutant peptide compared to tryptophan zipper 1 was identified. This research demonstrates the potential of using computational approaches to complement experimental methods in studying protein structures and their functional implications.

Quantifying the Impact of the Peptide Identification Framework on the Results of Fast Photochemical Oxidation of Protein Analysis.

Fast Photochemical Oxidation of Proteins (FPOP) is a promising technique for studying protein structure and dynamics. The quality of insight provided by FPOP depends on the reliability of the determination of the modification site. This study investigates the performance of two search engines, Mascot and PEAKS, for the data processing of FPOP analyses. Comparison of Mascot and PEAKS of the hemoglobin--haptoglobin Bruker timsTOF data set (PXD021621) revealed greater consistency in the Mascot identification of modified peptides, with around 26% of the IDs being mutual for all three replicates, compared to approximately 22% for PEAKS. The intersection between Mascot and PEAKS results revealed a limited number (31%) of shared modified peptides. Principal Component Analysis (PCA) using the peptide-spectrum match (PSM) score, site probability, and peptide intensity was applied to evaluate the results, and the analyses revealed distinct clusters of modified peptides. Mascot showed the ability to assess confident site determination, even with lower PSM scores. However, high PSM scores from PEAKS did not guarantee a reliable determination of the modification site. Fragmentation coverage of the modification position played a crucial role in Mascot assignments, while the AScore localizations from PEAKS often become ambiguous because the software employs MS/MS merging.

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

Gadolinium Spin Decoherence Mechanisms at High Magnetic Fields.

Favorable relaxation processes, high-field spectral properties, and biological compatibility have made spin-7/2 Gd3+-based spin labels an increasingly popular choice for protein structure studies using high-field electron paramagnetic resonance. However, high-field relaxation and decoherence in ensembles of half-integer high-spin systems, such as Gd3+, remain poorly understood. We report spin-lattice (T1) and phase memory (TM) relaxation times at 8.6 T (240 GHz), and we present the first comprehensive model of high-field, high-spin decoherence accounting for both the electron spin concentration and temperature. The model includes four principal mechanisms driving decoherence: energy-conserving electron spin flip-flops, direct "T1" spin-lattice relaxation-driven electron spin flip processes, indirect T1-driven flips of nearby electron spins, and nuclear spin flip-flops. Mechanistic insight into decoherence can inform the design of experiments making use of Gd3+ as spin probes or relaxivity agents and can be used to measure local average interspin distances as long as 17 nm.

Dimethylthiourea as a Quencher in Hydroxyl Radical Protein Footprinting Experiments.

Hydroxyl radical protein footprinting (HRPF) is a mass-spectrometry-based method for studying protein structures, interactions, conformations, and folding. This method is based on the irreversible labeling of solvent-exposed amino acid side chains by hydroxyl radicals. While catalase is commonly used as a quencher after the labeling of a protein by the hydroxyl radicals to efficiently remove the remaining hydrogen peroxide, it has some disadvantages. Catalase quenching adds a relatively high amount of protein to the sample, limiting the sensitivity of the method due to dynamic range issues and causing significant issues when dealing with more complex samples. We evaluated dimethylthiourea (DMTU) as a replacement for catalase in the quenching HRPF reactions. We observed that DMTU is highly effective at quenching HRPF oxidation. DMTU does not cause the background protein issues that catalase does, resulting in an increased number of protein identifications from complex mixtures. We recommend the replacement of catalase quenching with DMTU for all HRPF experiments.

Optimizing Phosphate Couplings for Dolichyl Diphosphochitobiose to Enable Protein N‐Glycosylation Studies

AbstractHerein we report an optimized synthesis for dolichyl diphosphochitobiose (GlcNAc2‐PP‐Dol25), a probe useful for biochemical and structural studies of protein N‐glycosylation in eukaryotic cells. We improved three phosphate coupling steps in terms of yields and reaction times, namely chitobiose phosphorylation, dolichol phosphorylation, and phosphate‐phosphate coupling, by adjusting reagents and conditions. We also developed an efficient preparative reverse‐phase HPLC purification protocol followed by ion exchange step to obtain the pure product as a stable sodium salt. These optimized procedures ensure a reliable supply of GlcNAc2‐PP‐Dol25 for enzymatic studies.

A Method for Calculating the Sign and Degree of Chirality of Supercoiled Protein Structures

Chirality plays an important role in studies of natural protein structures. Therefore, much attention is paid to solving the problems associated with the development of criteria and methods for assessing the chirality of biomolecules. In this paper, a new method for calculating the sign and degree of chirality of superhelices is proposed. The method makes it possible to characterize the chirality sign and to quantify coiled-coils and collagen superhelices. The degree of chirality is understood as a value indicating the intensity of twisting of individual helices around the axis of the superhelix. The calculation requires information about the relative spatial arrangement of the alpha carbon of the amino acid residues of the helices that make up the superhelix. The use of a small amount of raw data makes the method easy to apply, and the validity of the results of this study is confirmed through the analysis of real protein structures.

NextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography

Single-particle cryo-electron tomography is an emerging technique capable of determining the structure of proteins imaged within the native context of cells at molecular resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, we present a scalable, end-to-end framework for single-particle cryo-electron tomography data analysis from on-the-fly pre-processing of tilt series to high-resolution refinement and classification, which allows efficient analysis and visualization of datasets with hundreds of tilt series and hundreds of thousands of particles. We validate our approach using in vitro and cellular datasets, demonstrating its effectiveness at achieving high-resolution and revealing conformational heterogeneity in situ. The framework is made available through an intuitive and easy-to-use computer application, nextPYP (http://nextpyp.app).