Protein Structure Determination Research Articles

Integrating 19F Distance Restraints for Accurate Protein Structure Determination by Magic Angle Spinning NMR Spectroscopy.

Traditional protein structure determination by magic angle spinning (MAS) solid-state NMR spectroscopy primarily relies on interatomic distances up to 8 Å, extracted from 13C-, 15N-, and 1H-based dipolar-based correlation experiments. Here, we show that 19F fast (60 kHz) MAS NMR spectroscopy can supply additional, longer distances. Using 4F-Trp,U-13C,15N crystalline Oscillatoria agardhii agglutinin (OAA), we demonstrate that judiciously designed 2D and 3D 19F-based dipolar correlation experiments such as (H)CF, (H)CHF, and FF can yield interatomic distances in the 8-16 Å range. Incorporation of fluorine-based restraints into structure calculation improved the precision of Trp side chain conformations as well as regions in the protein around the fluorine containing residues, with notable improvements observed for residues in proximity to the Trp pairs (W10/W17 and W77/W84) in the carbohydrate-binding loops, which lacked sufficient long-range 13C-13C distance restraints. Our work highlights the use of fluorine and 19F fast MAS NMR spectroscopy as a powerful structural biology tool.

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces.

Protein-protein interactions are essential for a variety of biological phenomena including mediating bio-chemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination of single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction has still not been perfected. Additionally, experimentally determining structures is incredibly resource and time expensive. An alternative is the technique of computational docking, which takes the solved individual structures of proteins to produce candidate interfaces (decoys). Decoys are then scored using a mathematical function that assess the quality of the system, know as a scoring functions. Beyond docking, scoring functions are a critical component of assessing structures produced by many protein generative models. Scoring models are also used as a final filtering in many generative deep learning models including those that generate antibody binders, and those which perform docking. In this work we present improved scoring functions for protein-protein interactions which utilizes cutting-edge euclidean graph neural network architectures, to assess protein-protein interfaces. These euclidean docking score models are known as EuDockScore, and EuDockScore-Ab with the latter being antibody-antigen dock specific. Finally, we provided EuDockScore-AFM a model trained on antibody-antigen outputs from AlphaFold-Multimer which proves useful in re-ranking large numbers of AlphaFold-Multimer outputs. The code for these models is available at https://gitlab.com/mcfeemat/eudockscore.

The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.

Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX.

Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes.

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently.

DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

Development of a carbohydrate-binding protein prediction algorithm using structural features of stacking aromatic rings

Carbohydrate-protein interactions play fundamental roles in numerous aspects of biological activities, and the search for new carbohydrate (CHO)-binding proteins (CBPs) has long been a research focus. In this study, through the analysis of CBP structures, we identified significant enrichment of aromatic residues in CHO-binding regions. We further summarized the structural features of these aromatic rings within the CHO-stacking region, namely “exposing” and “proximity” features, and developed a screening algorithm that can identify CHO-stacking Trp (tryptophan) residues based on these two features. Our Trp screening algorithm can achieve high accuracy in both CBP (specificity score 0.93) and CBS (Carbohydrate binding site, precision score 0.77) prediction using experimentally determined protein structures. We also applied our screening algorithm on AlphaGO pan-species predicted models and observed significant enrichment of carbohydrate-related functions in predicted CBP candidates across different species. Moreover, through carbohydrate arrays, we experimentally verified the CHO-binding ability of four candidate proteins, which further confirms the robustness of the algorithm. This study provides another perspective on proteome-wide CBP and CBS prediction. Our results not only help to reveal the structural mechanism of CHO-binding, but also provide a pan-species CBP dataset for future CHO-protein interaction exploration.

Homonuclear Super-Resolution NMR Spectroscopy.

Homonuclear 1H NMR (nuclear magnetic resonance) spectra such as the [1H,1H]-NOESY (Nuclear Overhauser Enhancement spectroscopy) are essential for biomolecular structural biology. However, the spectrum contains hundreds to thousands of cross peaks meaning that within approximately 100 ppm2 there is significant signal overlap. Spectral resolution is thus a limiting factor for data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths' of cross peaks in [1H,1H]-NOESY and [1H,1H]-TOCSY spectra can be further reduced by a factor of 2-3 in each dimension by super-resolution spectroscopy. In the indirect dimension a composite exponential-cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function. Furthermore, measurement time saving by reduced-acquisition super-resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

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 13C-1H (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.

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.

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.