Protein Structure Determination Research Articles

Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm

Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.

Atomic resolution protein allostery from the multi-state structure of a PDZ domain

Recent methodological advances in solution NMR allow the determination of multi-state protein structures and provide insights into structurally and dynamically correlated protein sites at atomic resolution. This is demonstrated in the present work for the well-studied PDZ2 domain of protein human tyrosine phosphatase 1E for which protein allostery had been predicted. Two-state protein structures were calculated for both the free form and in complex with the RA-GEF2 peptide using the exact nuclear Overhauser effect (eNOE) method. In the apo protein, an allosteric conformational selection step comprising almost 60% of the domain was detected with an “open” ligand welcoming state and a “closed” state that obstructs the binding site by changing the distance between the β-sheet 2, α-helix 2, and sidechains of residues Lys38 and Lys72. The observed induced fit-type apo-holo structural rearrangements are in line with the previously published evolution-based analysis covering ~25% of the domain with only a partial overlap with the protein allostery of the open form. These presented structural studies highlight the presence of a dedicated highly optimized and complex dynamic interplay of the PDZ2 domain owed by the structure-dynamics landscape.

Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA

Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.

Predicting the Secondary Structure of Proteins: A Deep Learning Approach

Background: The machine learning computation paradigm touched new horizons with the development of deep learning architectures. It is widely used in complex problems and achieved significant results in many traditional applications like protein structure prediction, speech recognition, traffic management, health diagnostic systems and many more. Especially, Convolution neural network (CNN) has revolutionized visual data processing tasks. Objective: Protein structure is an important research area in various domains extending from medical science, health sectors to drug designing. Fourier Transform Infrared Spectroscopy (FTIR) is the leading tool for protein structure determination. This review aims to study the existing deep learning approaches proposed in the literature to predict proteins' secondary structure and to develop a conceptual relation between FTIR spectra images and deep learning models to predict the structure of proteins. Methods: Various pre-trained CNN models are identified and interpreted to correlate the FTIR images of proteins containing Amide-I and Amide-II absorbance values and their secondary structure. Results: The concept of transfer learning is efficiently incorporated using the models like Visual Geometry Group (VGG), Inception, Resnet, and Efficientnet. The dataset of protein spectra images is applied as input, and these models act significantly to predict the secondary structure of proteins. Conclusion: As deep learning is recently being explored in this field of research, it worked remarkably in this application and needs continuous improvement with the development of new models.

Yeast as a tool for membrane protein production and structure determination.

Membrane proteins are challenging targets to functionally and structurally characterize. An enduring bottleneck in their study is the reliable production of sufficient yields of stable protein. Here, we evaluate all eukaryotic membrane protein production experiments that have supported the deposition of a high-resolution structure. We focused on the most common yeast host systems, Saccharomyces cerevisiae and Pichia pastoris. The first high-resolution structure of a membrane protein produced in yeast was described in 1999 and today there are 186 structures of α-helical membrane proteins, representing 101 unique proteins from 37 families. Homologous and heterologous production are equally common in S. cerevisiae, while heterologous production dominates in P. pastoris, especially of human proteins, which represent about one-third of the total. Investigating protein engineering approaches (78 proteins from seven families) demonstrated that the majority contained a polyhistidine tag for purification, typically at the C-terminus of the protein. Codon optimization and truncation of hydrophilic extensions were also common approaches to improve yields. We conclude that yeast remains a useful production host for the study of α-helical membrane proteins.

Extensive Molecular Dynamics Simulations Disclosed the Stability of mPGES-1 Enzyme and the Structural Role of Glutathione (GSH) Cofactor.

A deep in silico investigation of various microsomal prostaglandin E2 synthase-1 (mPGES-1) protein systems is here reported using molecular dynamics (MD) simulations. Firstly, eight different proteins models (Models A-H) were built, starting from the active enzyme trimer system (Model A), namely that bound to three glutathione (GSH) cofactor molecules, and then gradually removing the GSHs (Models B-H), simulating each of them for 100 ns in explicit solvent. The analysis of the obtained data disclosed the structural role of GSH in the chemical architecture of mPGES-1 enzyme, thus suggesting the unlikely displacement of this cofactor, in accordance with experimentally determined protein structures co-complexed with small molecule inhibitors. Afterwards, Model A was submitted to microsecond-scale molecular dynamics simulation (total simulation time=10 μs), in order to shed light about the dynamical behaviour of this enzyme at atomic level and to obtain further structural features and protein function information. We confirmed the structural stability of the enzyme machinery, observing a conformational rigidity of the protein, with a backbone RMSD of ∼3 Å along the simulation time, and highlighting the strong active contribution of GSH molecules due to their active role in packing the protein chains through a tight binding at monomer interfaces. Furthermore, the focused analysis on R73 residue disclosed its role in solvent exchange events, probably excluding its function as route for GSH to enter towards the endoplasmic reticulum membrane, in line with the recently reported function of cap domain residues F44-D66 as gatekeeper for GSH entrance into catalytic site.

A coarse-grained approach to NMR-data-assisted modeling of protein structures.

The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4units higher to 14.4units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

ProTstab2 for Prediction of Protein Thermal Stabilities.

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

AICAR transformylase/IMP cyclohydrolase (ATIC) is essential for de novo purine biosynthesis and infection by Cryptococcus neoformans

The fungal pathogen Cryptococcus neoformans is a leading cause of meningoencephalitis in the immunocompromised. As current antifungal treatments are toxic to the host, costly, limited in their efficacy, and associated with drug resistance, there is an urgent need to identify vulnerabilities in fungal physiology to accelerate antifungal discovery efforts. Rational drug design was pioneered in de novo purine biosynthesis as the end products of the pathway, ATP and GTP, are essential for replication, transcription, and energy metabolism, and the same rationale applies when considering the pathway as an antifungal target. Here, we describe the identification and characterization of C. neoformans 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) transformylase/5′-inosine monophosphate cyclohydrolase (ATIC), a bifunctional enzyme that catalyzes the final two enzymatic steps in the formation of the first purine base inosine monophosphate. We demonstrate that mutants lacking the ATIC-encoding ADE16 gene are adenine and histidine auxotrophs that are unable to establish an infection in a murine model of virulence. In addition, our assays employing recombinantly expressed and purified C. neoformans ATIC enzyme revealed Km values for its substrates AICAR and 5-formyl-AICAR are 8-fold and 20-fold higher, respectively, than in the human ortholog. Subsequently, we performed crystallographic studies that enabled the determination of the first fungal ATIC protein structure, revealing a key serine-to-tyrosine substitution in the active site, which has the potential to assist the design of fungus-specific inhibitors. Overall, our results validate ATIC as a promising antifungal drug target.

Mass-selective and ice-free electron cryomicroscopy protein sample preparation via native electrospray ion-beam deposition.

Despite tremendous advances in sample preparation and classification algorithms for electron cryomicroscopy (cryo-EM) and single-particle analysis (SPA), sample heterogeneity remains a major challenge and can prevent access to high-resolution structures. In addition, optimization of preparation conditions for a given sample can be time-consuming. In the current work, it is demonstrated that native electrospray ion-beam deposition (native ES-IBD) is an alternative, reliable approach for the preparation of extremely high-purity samples, based on mass selection in vacuum. Folded protein ions are generated by native electrospray ionization, separated from other proteins, contaminants, aggregates, and fragments, gently deposited on cryo-EM grids, frozen in liquid nitrogen, and subsequently imaged by cryo-EM. We demonstrate homogeneous coverage of ice-free cryo-EM grids with mass-selected protein complexes. SPA reveals that the complexes remain folded and assembled, but variations in secondary and tertiary structures are currently limiting information in 2D classes and 3D EM density maps. We identify and discuss challenges that need to be addressed to obtain a resolution comparable to that of the established cryo-EM workflow. Our results show the potential of native ES-IBD to increase the scope and throughput of cryo-EM for protein structure determination and provide an essential link between gas-phase and solution-phase protein structures.

Serial femtosecond crystallography

With the advent of X-ray Free Electron Lasers (XFELs), new, high-throughput serial crystallography techniques for macromolecular structure determination have emerged. Serial femtosecond crystallography (SFX) and related methods provide possibilities beyond canonical, single-crystal rotation crystallography by mitigating radiation damage and allowing time-resolved studies with unprecedented temporal resolution. This primer aims to assist structural biology groups with little or no experience in serial crystallography planning and carrying out a successful SFX experiment. It discusses the background of serial crystallography and its possibilities. Microcrystal growth and characterization methods are discussed, alongside techniques for sample delivery and data processing. Moreover, it gives practical tips for preparing an experiment, what to consider and do during a beamtime and how to conduct the final data analysis. Finally, the Primer looks at various applications of SFX, including structure determination of membrane proteins, investigation of radiation damage-prone systems and time-resolved studies.

The Protein Fusion Strategy Facilitates the Structure Determination of Small Membrane Proteins by Cryo-EM.

Despite the resolution revolution of cryo-EM, structures of small membrane proteins (<80 kDa) are still understudied. These proteins are notoriously reluctant to structure determination by single-particle cryo-EM. Protein fusion might represent a plausible strategy to overcome such difficulties. This Perspective enumerates recent exemplary progress and discusses the future potential of the protein fusion strategy.

An improved, time-efficient approach to extract accurate distance restraints for NMR2 structure calculation.

Exact nuclear Overhauser enhancement (eNOE) yields highly accurate, ensemble averaged H-H distance restraints with an accuracy of up to 0.1 Å for the multi-state structure determination of proteins as well as for nuclear magnetic resonance molecular replacement (MR) to determine the structure of the protein-ligand interaction site in a time-efficient manner. However, in the latter application, the acquired eNOEs lack the obtainable precision of 0.1 Å because of the asymmetrical nature of the filtered nuclear Overhauser enhancement spectroscopy (NOESY) experiment used in MR. This error is further propagated to the eNOE equations used to fit and extract the distance restraints. In this work, a new analysis method is proposed to obtain inter-molecular distance restraints from the filtered NOESY spectrum more accurately and intuitively by dividing the NOE cross peak by the corresponding diagonal peak of the ligand. The method termed diagonal-normalised eNOEs was tested on the data acquired by on the complex of PIN1 and a small, weak-binding phenylimidazole fragment. MR calculations performed using the distances derived from diagonal-normalised eNOEs yielded the right orientation of the fragment in the binding pocket and produced a structure that more closely resembles the benchmark X-ray structure (2XP6) with an average heavy-atom root-mean-square deviation (RMSD) of 1.681 Å with respect to it, when compared to the one produced with traditional MR with an average heavy atom RMSD of 3.628 Å. This is attributed to the higher precision of the evaluated distance restraints.

Novel designed rigidified imaging scaffolds for high-resolution structure determination of small proteins with cryo-EM

Novel designed rigidified imaging scaffolds for high-resolution structure determination of small proteins with cryo-EM

A comprehensive strategy for quick determination of protein structures by MR-native SAD method

A comprehensive strategy for quick determination of protein structures by MR-native SAD method

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction.

Cryo-EM Structure of an Atypical Proton-Coupled Peptide Transporter: Di- and Tripeptide Permease C.

Proton-coupled Oligopeptide Transporters (POTs) of the Major Facilitator Superfamily (MFS) mediate the uptake of short di- and tripeptides in all phyla of life. POTs are thought to constitute the most promiscuous class of MFS transporters, with the potential to transport more than 8400 unique substrates. Over the past two decades, transport assays and biophysical studies have shown that various orthologues and paralogues display differences in substrate selectivity. The E. coli genome codes for four different POTs, known as Di- and tripeptide permeases A-D (DtpA-D). DtpC was shown previously to favor positively charged peptides as substrates. In this study, we describe, how we determined the structure of the 53 kDa DtpC by cryogenic electron microscopy (cryo-EM), and provide structural insights into the ligand specificity of this atypical POT. We collected and analyzed data on the transporter fused to split superfolder GFP (split sfGFP), in complex with a 52 kDa Pro-macrobody and with a 13 kDa nanobody. The latter sample was more stable, rigid and a significant fraction dimeric, allowing us to reconstruct a 3D volume of DtpC at a resolution of 2.7 Å. This work provides a molecular explanation for the selectivity of DtpC, and highlights the value of small and rigid fiducial markers such as nanobodies for structure determination of low molecular weight integral membrane proteins lacking soluble domains.

Methyl probes in proteins for determining ligand binding mode in weak protein–ligand complexes

Structures of protein–ligand complexes provide critical information for drug design. Most protein–ligand complex structures are determined using X-ray crystallography, but where crystallography is not able to generate a structure for a complex, NMR is often the best alternative. However, the available tools to enable rapid and robust structure determination of protein–ligand complexes by NMR are currently limited. This leads to situations where projects are either discontinued or pursued without structural data, rendering the task more difficult. We previously reported the NMR Molecular Replacement (NMR2) approach that allows the structure of a protein–ligand complex to be determined without requiring the cumbersome task of protein resonance assignment. Herein, we describe the NMR2 approach to determine the binding pose of a small molecule in a weak protein–ligand complex by collecting sparse protein methyl-to-ligand NOEs from a selectively labeled protein sample and an unlabeled ligand. In the selective labeling scheme all methyl containing residues of the protein are protonated in an otherwise deuterated background. This allows measurement of intermolecular NOEs with greater sensitivity using standard NOESY pulse sequences instead of isotope-filtered NMR experiments. This labelling approach is well suited to the NMR2 approach and extends its utility to include larger protein–ligand complexes.

Detergents and alternatives in cryo-EM studies of membrane proteins.

Structure determination of membrane proteins has been a long-standing challenge to understand the molecular basis of life processes. Detergents are widely used to study the structure and function of membrane proteins by various experimental methods, and the application of membrane mimetics is also a prevalent trend in the field of cryo-EM analysis. This review focuses on the widely-used detergents and corresponding properties and structures, and also discusses the growing interests in membrane mimetic systems used in cryo-EM studies, providing insights into the role of detergent alternatives in structure determination.

Fast Magic-Angle-Spinning NMR Reveals the Evasive Hepatitis B Virus Capsid C-Terminal Domain.

Experimentally determined protein structures often feature missing domains. One example is the C‐terminal domain (CTD) of the hepatitis B virus capsid protein, a functionally central part of this assembly, crucial in regulating nucleic‐acid interactions, cellular trafficking, nuclear import, particle assembly and maturation. However, its structure remained elusive to all current techniques, including NMR. Here we show that the recently developed proton‐detected fast magic‐angle‐spinning solid‐state NMR at >100 kHz MAS allows one to detect this domain and unveil its structural and dynamic behavior. We describe the experimental framework used and compare the domain's behavior in different capsid states. The developed approaches extend solid‐state NMR observations to residues characterized by large‐amplitude motion on the microsecond timescale, and shall allow one to shed light on other flexible protein domains still lacking their structural and dynamic characterization.