Protein Structure Determination Research Articles

Utilizing anomalous signals for element identification in macromolecular crystallography.

AlphaFold2 has revolutionized structural biology by offering unparalleled accuracy in predicting protein structures. Traditional methods for determining protein structures, such as X-ray crystallography and cryo-electron microscopy, are often time-consuming and resource-intensive. AlphaFold2 provides models that are valuable for molecular replacement, aiding in model building and docking into electron density or potential maps. However, despite its capabilities, models from AlphaFold2 do not consistently match the accuracy of experimentally determined structures, need to be validated experimentally and currently miss some crucial information, such as post-translational modifications, ligands and bound ions. In this paper, the advantages are explored of collecting X-ray anomalous data to identify chemical elements, such as metal ions, which are key to understanding certain structures and functions of proteins. This is achieved through methods such as calculating anomalous difference Fourier maps or refining the imaginary component of the anomalous scattering factor f''. Anomalous data can serve as a valuable complement to the information provided by AlphaFold2 models and this is particularly significant in elucidating the roles of metal ions.

Enhancing solution structural analysis of large molecular proteins through optimal stereo array isotope labeling of aromatic amino acids

The observation of side-chain peaks of aromatic amino acids is the prerequisite for a high-resolution three-dimensional structure determination of proteins by NMR. However, it becomes difficult with increasing molecular size due to an increased transverse relaxation and the control of the relaxation pathway is needed to achieve the observation. We demonstrated that even for the large molecular size of 82 kDa Malate synthase G (MSG), the aromatic 13C1H (CH) peaks of Tryptophan (Trp) and Phenylalanine (Phe) residues can be observed with high quality using a systematic stable isotope labeling scheme, Stereo-Array Isotope Labeling (SAIL) method. However, the sequence specific assignments of these peaks relied on the use of amino acid substitutions, employing an inefficient method that required many isotopes labeled samples. In this study, we developed novel SAIL amino acids that allow for the observation of the aromatic ring δ,ζ and the aliphatic β position peak of Phe residues. The application of TROSY-based experiment to the isolated CH moieties resulted in the successful observation of discernible and resolved CH peaks in Phe residues in MSG. In MSG, the sequence-specific assignments of the backbone and Cβ positions have already been confirmed. Therefore, using this labeling method, the δ and β position peaks of Phe residues can be clearly assigned in a sequence-specific and stereospecific manner through experiments based on intra-residue NOE. Furthermore, the NOESY experiment also allows for the acquisition of information pertaining to the conformation of Phe residues, such as the χ1 dihedral angle, providing valuable insights for the determination of accurate protein structures and in dynamic analysis. This new SAIL amino acids open an avenue to achieve a variety of NMR analysis of large molecular proteins, including a high-resolution structure determination and dynamics and interaction analysis.

StrIDR: a database of intrinsically disordered regions of proteins with experimentally resolved structures.

Intrinsically disordered regions (IDRs) of proteins exist as an ensemble of conformations, and not as a single structure. Existing databases contain extensive, experimentally derived annotations of intrinsic disorder for millions of proteins at the sequence level. However, only a tiny fraction of these IDRs are associated with an experimentally determined protein structure. Moreover, even if a structure exists, parts of the disordered regions may still be unresolved. Here we organize Structures of Intrinsically Disordered Regions (StrIDR), a database of IDRs confirmed via experimental or homology-based evidence, resolved in experimentally determined structures. The database can provide useful insights into the dynamics, folding, and interactions of IDRs. It can also facilitate computational studies on IDRs, such as those using molecular dynamics simulations and/or machine learning. StrIDR is available at https://isblab.ncbs.res.in/stridr. The web UI allows for downloading PDB structures and SIFTS mappings of individual entries. Additionally, the entire database can be downloaded in a JSON format. The source code for creating and updating the database is available at https://github.com/isblab/stridr.

N-terminal domain of polypyrimidine-tract binding protein is a dynamic folding platform for adaptive RNA recognition.

The N-terminal RNA recognition motif domain (RRM1) of polypyrimidine tract binding protein (PTB) forms an additional C-terminal helix α3, which docks to one edge of the β-sheet upon binding to a stem-loop RNA containing a UCUUU pentaloop. Importantly, α3 does not contact the RNA. The α3 helix therefore represents an allosteric means to regulate the conformation of adjacent domains in PTB upon binding structured RNAs. Here we investigate the process of dynamic adaptation by stem-loop RNA and RRM1 using NMR and MD in order to obtain mechanistic insights on how this allostery is achieved. Relaxation data and NMR structure determination of the free protein show that α3 is partially ordered and interacts with the domain transiently. Stem-loop RNA binding quenches fast time scale dynamics and α3 becomes ordered, however microsecond dynamics at the protein-RNA interface is observed. MD shows how RRM1 binding to the stem-loop RNA is coupled to the stabilization of the C-terminal helix and helps to transduce differences in RNA loop sequence into changes in α3 length and order. IRES assays of full length PTB and a mutant with altered dynamics in the α3 region show that this dynamic allostery influences PTB function in cultured HEK293T cells.

Investigating Lipase/Stain Interactions: Determining Interfacial Protein Conformation with Surface Spectroscopy.

Conventional bulk protein structure determination methods are not suitable for understanding the distinct and diverse interactions of proteins with interfaces. Notably, interfacial activation is a feature common to many lipases involving movement of a helical "lid" region upon contact with a hydrophobic surface to expose the catalytic site. Here we use the surface specificity of vibrational sum frequency generation spectroscopy (VSFG) spectroscopy to directly probe the conformation of Thermomyces lanuginosus lipase (TLL) at hydrophobic interfaces. The TLL-catalyzed reaction at the air/water interface is monitored by VSFG spectroscopy, showing loss of ester carbonyl modes and appearance of carboxylate stretching modes of the fatty acid products. Furthermore, comparison of experimental and calculated VSFG spectra of the amide I band of TLL allows us to discern the subtle structural changes involved with lid-opening at a hydrophobic surface. Finally, we report a likely orientation of this lid-open state, which interacts with the surface through a loop region away from the lid and active site. This experimental framework for probing protein structure and function at interfaces addresses a significant problem in protein science that is not only impeding the design of better enzymes for biotechnology applications but also drug discovery targeting membrane associated proteins.

A comprehensive guide for secondary structure and tertiary structure determination in peptides and proteins by circular dichroism spectrometer.

Secondary structure refers to highly regular local sub-structures formed by the polypeptide backbone through hydrogen bonding. The two main types of secondary structures are α-helices and β-strands (which can form β-sheets). The development of a robust circular dichroism (CD) method for structural analysis of biomolecules requires careful consideration of several key factors. Solvent selection plays a crucial role in maintaining the native or desired conformation of the sample while ensuring transparency in the relevant wavelength regions. Aqueous buffers are often preferred for studying proteins in their native state. Optimizing the sample concentration and path length is essential to achieve an optimal absorbance range and maximize the signal-to-noise ratio. Typical concentrations for far-UV CD measurements range from 0.1 to 1mg/ml, with shorter path lengths (1mm) allowing for higher concentrations and longer path lengths (5mm) suitable for dilute solutions. Instrumental parameters, such as scanning speed, accumulations, and nitrogen flow rate, significantly impact the quality and reliability of the acquired CD spectra. Data processing is a critical step in obtaining accurate and interpretable CD spectra. Baseline correction, smoothing, and conversion to mean residue ellipticity are essential for reliable secondary structure analysis.

NDT-C11 as a Viable Novel Detergent for Single Particle Cryo-EM.

Single particle cryo electron microscopy (cryo-EM) is now the major method for the determination of integral membrane protein structure. For the success of a given project the type of membrane mimetic used for extraction from the native cell membrane, purification to homogeneity and finally cryo-grid vitrification is crucial. Although small molecule amphiphiles - detergents - are the most widely used membrane mimetic, specific tailoring of detergent structure for single particle cryo-EM is rare and the demand for effective detergents not satisfied. Here, we compare the popular detergent lauryl maltose-neopentyl glycol (LMNG) with the novel detergent neopentyl glycol-derived triglucoside-C11 (NDT-C11) in its behavior as free detergent and when bound to two types of multisubunit membrane protein complexes - cyanobacterial photosystem I (PSI) and mammalian F-ATP synthase. We conclude that NDT-C11 has high potential to become a very useful detergent for single particle cryo-EM of integral membrane proteins.

Leveraging coevolutionary insights and AI-based structural modeling to unravel receptor–peptide ligand-binding mechanisms

Secreted signaling peptides are central regulators of growth, development, and stress responses, but specific steps in the evolution of these peptides and their receptors are not well understood. Also, the molecular mechanisms of peptide-receptor binding are only known for a few examples, primarily owing to the limited availability of protein structural determination capabilities to few laboratories worldwide. Plants have evolved a multitude of secreted signaling peptides and corresponding transmembrane receptors. Stress-responsive SERINE RICH ENDOGENOUS PEPTIDES (SCOOPs) were recently identified. Bioactive SCOOPs are proteolytically processed by subtilases and are perceived by the leucine-rich repeat receptor kinase MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2 (MIK2) in the model plant Arabidopsis thaliana. How SCOOPs and MIK2 have (co)evolved, and how SCOOPs bind to MIK2 are unknown. Using in silico analysis of 350 plant genomes and subsequent functional testing, we revealed the conservation of MIK2 as SCOOP receptor within the plant order Brassicales. We then leveraged AI-based structural modeling and comparative genomics to identify two conserved putative SCOOP-MIK2 binding pockets across Brassicales MIK2 homologues predicted to interact with the "SxS" motif of otherwise sequence-divergent SCOOPs. Mutagenesis of both predicted binding pockets compromised SCOOP binding to MIK2, SCOOP-induced complex formation between MIK2 and its coreceptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1, and SCOOP-induced reactive oxygen species production, thus, confirming our in silico predictions. Collectively, in addition to revealing the elusive SCOOP-MIK2 binding mechanism, our analytic pipeline combining phylogenomics, AI-based structural predictions, and experimental biochemical and physiological validation provides a blueprint for the elucidation of peptide ligand-receptor perception mechanisms.

A curated rotamer library for common post-translational modifications of proteins.

Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However, much of protein function is modulated beyond the translated sequence through the introduction of post-translational modifications (PTMs). In this work, we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro, Rosetta, and AlphaFold3 in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions. The codes for dihedral angle computations and library creation are available at https://github.com/THGLab/ptm_sc.git.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

The determination of a protein’s structure is often a first step towards the development of a mechanistic understanding of its function. Considerable advances in computational protein structure prediction have been made in recent years, with AlphaFold2 (AF2) emerging as the primary tool used by researchers for this purpose. While AF2 generally predicts accurate structures of folded proteins, we present here a case where AF2 incorrectly predicts the structure of a small, folded and compact protein with high confidence. This protein, pro-interleukin-18 (pro-IL-18), is the precursor of the cytokine IL-18. Interestingly, the structure of pro-IL-18 predicted by AF2 matches that of the mature cytokine, and not the corresponding experimentally determined structure of the pro-form of the protein. Thus, while computational structure prediction holds immense promise for addressing problems in protein biophysics, there is still a need for experimental structure determination, even in the context of small well-folded, globular proteins.

Cooperative Gsx2-DNA binding requires DNA bending and a novel Gsx2 homeodomain interface.

The conserved Gsx homeodomain (HD) transcription factors specify neural cell fates in animals from flies to mammals. Like many HD proteins, Gsx factors bind A/T-rich DNA sequences prompting the following question: How do HD factors that bind similar DNA sequences in vitro regulate specific target genes in vivo? Prior studies revealed that Gsx factors bind DNA both as a monomer on individual A/T-rich sites and as a cooperative homodimer to two sites spaced precisely 7 bp apart. However, the mechanistic basis for Gsx-DNA binding and cooperativity is poorly understood. Here, we used biochemical, biophysical, structural and modeling approaches to (i) show that Gsx factors are monomers in solution and require DNA for cooperative complex formation, (ii) define the affinity and thermodynamic binding parameters of Gsx2/DNA interactions, (iii) solve a high-resolution monomer/DNA structure that reveals that Gsx2 induces a 20° bend in DNA, (iv) identify a Gsx2 protein-protein interface required for cooperative DNA binding and (v) determine that flexible spacer DNA sequences enhance Gsx2 cooperativity on dimer sites. Altogether, our results provide a mechanistic basis for understanding the protein and DNA structural determinants that underlie cooperative DNA binding by Gsx factors.

Unlocking the power of AI models: exploring protein folding prediction through comparative analysis.

Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study's main objective is to assess the accuracy of the model's predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.

NPEX: Never give up protein exploration with deep reinforcement learning

Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain Monte Carlo simulations, which require untenably long calculation times and prior structural knowledge. Here, we developed an innovative method for protein structure determination called never give up protein exploration (NPEX) with deep reinforcement learning. The NPEX method leverages the soft actor-critic algorithm and the intrinsic reward system, effectively adding a bias potential without the need for prior knowledge. To demonstrate the method's effectiveness, we applied it to four models: a double well, a triple well, the alanine dipeptide, and the tryptophan cage. Compared with Markov chain Monte Carlo simulations, NPEX had markedly greater sampling efficiency. The significantly enhanced computational efficiency and lack of prior domain knowledge requirements of the NPEX method will revolutionize protein structure exploration.

Plant protein-lipid interfaces studied by molecular dynamics simulations.

The delineation of protein-lipid interfaces is essential for understanding the mechanisms of various membrane-associated processes crucial to plant development and growth, including signalling, trafficking, and membrane transport. Due to their highly dynamic nature, the precise characterization of lipid-protein interactions by experimental techniques is challenging. Molecular dynamics simulations provide a powerful computational alternative with a spatial-temporal resolution allowing the atomistic-level description. In this review, we aim to introduce plant scientists to molecular dynamics simulations. We describe different steps of performing molecular dynamics simulations and provide a broad survey of molecular dynamics studies investigating plant protein-lipid interfaces. Our aim is also to illustrate that combining molecular dynamics simulations with artificial intelligence-based protein structure determination opens up unprecedented possibilities for future investigations of dynamic plant protein-lipid interfaces.

Identification of domains in Plasmodium falciparum proteins of unknown function using DALI search on AlphaFold predictions

Plasmodium falciparum, the causative agent of malaria, poses a significant global health challenge, yet much of its biology remains elusive. A third of the genes in the P. falciparum genome lack annotations regarding their function, impeding our understanding of the parasite's biology. In this study, we employ structure predictions and the DALI search algorithm to analyse proteins encoded by uncharacterized genes in the reference strain 3D7 of P. falciparum. By comparing AlphaFold predictions to experimentally determined protein structures in the Protein Data Bank, we found similarities to known domains in 353 proteins of unknown function, shedding light on their potential functions. The lowest-scoring 5% of similarities were additionally validated using the size-independent TM-align algorithm, confirming the detected similarities in 88% of the cases. Notably, in over 70 P. falciparum proteins the presence of domains resembling heptatricopeptide repeats, which are typically involvement in RNA binding and processing, was detected. This suggests this family, which is important in transcription in mitochondria and apicoplasts, is much larger in Plasmodium parasites than previously thought. The results of this domain search provide a resource to the malaria research community that is expected to inform and enable experimental studies.

The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited.

Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets.

Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data.

Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Programmatic access to the 3D-Beacons API Basic Protocol 2: Sequence-based search using the 3D-Beacons API Basic Protocol 3: Accessing macromolecules from 3D-Beacons with JalView Basic Protocol 4: Enhancing data accessibility through 3D-Beacons.

X-ray crystal structure of a designed rigidified imaging scaffold in the ligand-free conformation.

Imaging scaffolds composed of designed protein cages fused to designed ankyrin repeat proteins (DARPins) have enabled the structure determination of small proteins by cryogenic electron microscopy (cryo-EM). One particularly well characterized scaffold type is a symmetric tetrahedral assembly composed of 24 subunits, 12 A and 12 B, which has three cargo-binding DARPins positioned on each vertex. Here, the X-ray crystal structure of a representative tetrahedral scaffold in the apo state is reported at 3.8 Å resolution. The X-ray crystal structure complements recent cryo-EM findings on a closely related scaffold, while also suggesting potential utility for crystallographic investigations. As observed in this crystal structure, one of the three DARPins, which serve as modular adaptors for binding diverse `cargo' proteins, present on each of the vertices is oriented towards a large solvent channel. The crystal lattice is unusually porous, suggesting that it may be possible to soak crystals of the scaffold with small (≤30 kDa) protein cargo ligands and subsequently determine cage-cargo structures via X-ray crystallography. The results suggest the possibility that cryo-EM scaffolds may be repurposed for structure determination by X-ray crystallography, thus extending the utility of electron-microscopy scaffold designs for alternative structural biology applications.

Broadening environmental research in the era of accurate protein structure determination and predictions

Broadening environmental research in the era of accurate protein structure determination and predictions