Proteins Of Known Structure Research Articles

EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

Structure and ligand binding in the putative anti-microbial peptide transporter protein, YejA.

YejABEF is an ATP-binding cassette transporter that is implicated in the sensitivity of Escherichia coli to anti-microbial peptides, the best-characterized example being microcin C, a peptide-nucleotide antibiotic that targets aspartyl-tRNA synthetase. Here the structure of the extracellular solute binding protein, YejA, has been determined, revealing an oligopeptide-binding protein fold enclosing a ligand-binding pocket larger than those of other peptide-binding proteins of known structure. Prominent electron density in this cavity defines an undecapeptide sequence LGEPRYAFNFN, an observation that is confirmed by mass spectrometry. In the structure, the peptide interactions with the protein are mediated by main chain hydrogen bonds with the exception of Arg5 whose guanidinium side chain makes a set of defining polar interactions with four YejA residues. More detailed characterization of purified recombinant YejA, by a combination of ESI and MALDI-mass spectrometry as well as thermal shift assays, reveals a set of YejA complexes containing overlapping peptides 10-19 residues in length. All contain the sequence LGEPRYAFN. Curiously, these peptides correspond to residues 8-26 of the mature YejA protein, which belong to a unique N-terminal extension that distinguishes YejA from other cluster C oligopeptide binding proteins of known structure. This 35-residue extension is well-ordered and packs across the surface of the protein. The undecapeptide ligand occupies only a fraction of the enclosed pocket volume suggesting the possibility that much larger peptides or peptide conjugates could be accommodated, though thermal shift assays of YejA binding to antimicrobial peptides and peptides unrelated to LGEPRYAFNFN have not provided evidence of binding. While the physiological significance of this 'auto-binding' is not clear, the experimental data suggest that it is not an artefact of the crystallization process and that it may have a function in the sensing of periplasmic or membrane stress.

STRUCTURAL PREDICTION OF HUMAN ZIP 2 AND ZIP4 BASED ON HOMOLOGY MODELLING AND MOLECULAR SIMULATION

Objective: This study aimed to analyze the structural proteins of zinc transporters as the target for drug actions and their molecular interactions. Methods: The present study is about the homology modelling and analysis of the zinc transporter function using the in silico molecular modelling method. Homology modelling predicts the 3D structure of a protein based on the sequence alignment with one or more template proteins of known structure. This study using in silico molecular modelling method, explains the 3D structure of human ZIP 2 and ZIP4 with Ramachandran Plot analysis, physical and chemical characteristics, transmembrane prediction with structural biology, and binding site prediction through molecular docking simulation. Results: Based on the physicochemical properties of the 3D structure of the ZIP2 and ZIP4 proteins, each comprises 309 amino acids and 582 amino acids with pI values of 5.85 and 5.24. The amino acid composition analysis showed that both proteins contain many Leucine amino acids. The Ramachandran diagram concludes that both proteins are stable in the stereochemical conformation forming a secondary structure. The binding amino acids on ZIP2 include Glu281, His216, Ser284, and Arg46. The binding amino acids in ZIP4 include Gln148, Gln154, Thr155, His197, Ala138, and Lys157. Conclusion: Establishment of the structure and function of human ZIP2 and ZIP4 as zinc transporters in cell membranes and prediction of ZIP2 and ZIP4 binding sites through molecular dcoking.

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods

The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track cis peptides, π-helices, 310-helices, and other small 3D motifs that occur in the PDB database at a frequency of lower than 1% of total amino acid residues. The best-performing AI method, AlphaFold2, captured these uncommon structural elements exquisitely well. All discrepancies appeared to be a consequence of crystal environment effects. We propose that the neural network learned a protein structure potential of mean force, enabling it to correctly identify situations where unusual structural features represent the lowest local free energy because of subtle influences from the atomic environment.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters.

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD1 exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD1-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD1. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD1 and the redesigned 1a8uD1-M8 were devised from scratch. More importantly, the designed 1a8uD1-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Abstract PL05-03: What if all your favorite proteins are druggable

Abstract Many proteins that play key roles in cancer are considered difficult drug targets, if not entirely undruggable, due to an apparent lack of pockets where small molecules can bind with the affinity and specificity required for a drug. ‘Cryptic’ pockets that are absent in known structures of proteins but form due to protein dynamics could alleviate this problem. However, it has been hard to assess or exploit the opportunities that cryptic pockets present due to the inherent difficulty in identifying such pockets. Here, I will discuss progress from my lab on combining biophysical experiments, computer simulations, and machine learning to identify and target cryptic pockets. In one case study, we have used computer simulations deployed on the Folding@home distributed computing environment to predict a cryptic pocket that can allosterically control a protein-RNA interaction, and then experimentally confirmed our predictions. Based on data from this study and others in our lab, we have trained a machine learning algorithm to predict where cryptic pockets are likely to form from single protein structures, enabling us to assess how prevalent cryptic pockets are on a large scale. Citation Format: Gregory R. Bowman. What if all your favorite proteins are druggable. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr PL05-03.

Structure of the T. brucei kinetoplastid RNA editing substrate-binding complex core component, RESC5.

Kinetoplastid protists such as Trypanosoma brucei undergo an unusual process of mitochondrial uridine (U) insertion and deletion editing termed kinetoplastid RNA editing (kRNA editing). This extensive form of editing, which is mediated by guide RNAs (gRNAs), can involve the insertion of hundreds of Us and deletion of tens of Us to form a functional mitochondrial mRNA transcript. kRNA editing is catalyzed by the 20 S editosome/RECC. However, gRNA directed, processive editing requires the RNA editing substrate binding complex (RESC), which is comprised of 6 core proteins, RESC1-RESC6. To date there are no structures of RESC proteins or complexes and because RESC proteins show no homology to proteins of known structure, their molecular architecture remains unknown. RESC5 is a key core component in forming the foundation of the RESC complex. To gain insight into the RESC5 protein we performed biochemical and structural studies. We show that RESC5 is monomeric and we report the T. brucei RESC5 crystal structure to 1.95 Å. RESC5 harbors a dimethylarginine dimethylaminohydrolase-like (DDAH) fold. DDAH enzymes hydrolyze methylated arginine residues produced during protein degradation. However, RESC5 is missing two key catalytic DDAH residues and does bind DDAH substrate or product. Implications of the fold for RESC5 function are discussed. This structure provides the first structural view of an RESC protein.

The BASIL cure: Using structure to predict function in protein biochemistry.

We have developed an undergraduate biochemistry lab curriculum based on authentic inquiry. This course-based undergraduate research experience (CURE) allows a large number of students to gain skills often learned only in the traditional one-on-one mentor-student research model by bringing a research project into the undergraduate teaching lab. The Biochemistry Authentic Scientific Inquiry Lab (BASIL) uses a combined computational and wet lab approach to study proteins of known structure but unknown function. Over 3800 structures in the Protein Data Bank (PDB) have unknown function. Students following the BASIL curriculum use a combination of sequence and structure alignment tools to study these structures with the goal of identifying possible enzymes. They then use molecular docking to predict what model substrates fit near a proposed active site. Students can produce the target enzymes in the lab using standard wet-lab biochemistry techniques for expression and purification, and they then perform kinetic assays with model substrates selected from their docking studies. We have successfully used this curriculum in biochemistry lab courses for majors and non-majors. The curriculum is modular and can be used as a whole or individual parts may be incorporated into an existing course - either lecture or lab. Curriculum materials are available free-of-charge at basilbiochem.org. We are currently investigating how this curriculum can be used in a variety of academic settings, and we welcome new collaborators. We can offer synchronous support via virtual meetings and asynchronous support via Slack. This project is supported in part by NSF IUSE 2141908.

Computational protein structure elucidation from covalent labeling mass spectrometry data.

Mass spectrometry (MS)-based methods such as covalent labeling MS are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution structure determination methods, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure, such as solvent exposure of individual residues or location of protein-protein interfaces. However, easy and reliable translation of this information into accurate structural protein and protein complex models remains particularly challenging, due to a lack of standardized and automated methods. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data. My group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. We have successfully implemented the use of MS covalent labeling data into the Rosetta software to predict protein and protein complex structure guided by the experimental data. We developed algorithms to predict protein structure from hydroxyl radical and diethylpyrocarbonate (DEPC) labeling data. To evaluate the effectiveness of our covalent-labeling guided structure prediction algorithms, we benchmarked their performance using proteins of known structure for which experimental covalent labeling data exists. For all benchmark proteins, better quality models were built when MS data was used in the scoring. For modeling with hydroxyl radical labeling data, we measured exposure of labeled residues, while in the case of DEPC labeling we further assessed the local hydrophobic microenvironment of serine, threonine, and tyrosine residues. We are now extending this work to use MS data in deep learning-based predictions of protein structure.

Structures of distantly related interacting protein homologs are less divergent than non-interacting homologs.

Homologous proteins can display high structural variation due to evolutionary divergence at low sequence identity. This classical inverse relationship between sequence identity and structural similarity, established many years ago, has remained true between homologous proteins of known structure over time. However, a large number of heteromeric proteins also exist in the structural data bank, where the interacting subunits belong to the same fold and maintain low sequence identity between themselves. It is not clear if there is any selection pressure to deviate from the inverse sequence-structure relationship for such interacting distant homologs, in comparison to pairs of homologs which are not known to interact. We examined 12,824 fold pairs of interacting homologs of known structure, which includes both heteromers and multi-domain proteins. These were compared with monomeric proteins, resulting in 26,082 fold pairs as a dataset of non-interacting homologous systems. Interacting homologs were found to retain higher structural similarity than non-interacting homologs at diminishing sequence identity in a statistically significant manner. Interacting homologs are more similar in their 3D structures than non-interacting homologs and have a preference towards symmetric association. There appears to be a structural constraint between remote homologs due to this commitment.

Validated determination of NRG1 Ig-like domain structure by mass spectrometry coupled with computational modeling

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

Proteins with Known Structure but Previously Unknown Function Identified as Hydrolases

The Protein Data Bank (PDB) contains the structures of over 4000 proteins of unknown function. In an attempt to assign function to some of these structures we combined in silico and in vitro analyses focusing on those most likely to be of the hydrolase class. We used bioinformatics tools including active site‐alignment software, BLAST, Pfam, and Dali to predict putative protein functions. We expressed and purified the proteins and carried out in vitro activity assays, informed by the in silico‐predicted enzyme function. We present data confirming the in vitro hydrolase function of several proteins of known structure but previously unidentified function. These studies used the Biochemistry Authentic Scientific Inquiry Lab (BASIL) modules as a framework and were carried out as part of an undergraduate course‐based research experience (CURE).

Using structure to predict function in protein biochemistry: a combined computational and wet lab approach

We have developed an undergraduate biochemistry lab curriculum based on authentic inquiry. The Biochemistry Authentic Scientific Inquiry Lab (BASIL) uses a combined computational and wet lab approach to study proteins of known structure but unknown function. There are over 3800 structures in the Protein Data Bank (PDB) that have unknown function. Students use a combination of sequence and structure alignment tools to study these structures with the goal of identifying possible enzymes. They then use molecular docking to predict what model substrates fit near a proposed active site.

Crystal structure of a hypothetical protein from Giardia lamblia.

Giardiasis is the most prevalent diarrheal disease globally and affects humans and animals. It is a significant problem in developing countries, the number one cause of travelers' diarrhea and affects children and immunocompromised individuals, especially HIV-infected individuals. Giardiasis is treated with antibiotics (tinidazole and metronidazole) that are also used for other infections such as trichomoniasis. The ongoing search for new therapeutics for giardiasis includes characterizing the structure and function of proteins from the causative protozoan Giardia lamblia. These proteins include hypothetical proteins that share 30% sequence identity or less with proteins of known structure. Here, the atomic resolution structure of a 15.6 kDa protein was determined by molecular replacement. The structure has the two-layer αβ-sandwich topology observed in the prototypical endoribonucleases L-PSPs (liver perchloric acid-soluble proteins) with conserved allosteric active sites containing small molecules from the crystallization solution. This article is an educational collaboration between Hampton University and the Seattle Structural Genomics Center for Infectious Disease.

SCOPe: improvements to the structural classification of proteins - extended database to facilitate variant interpretation and machine learning.

The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

Membrane transporter dimerization driven by differential lipid solvation energetics of dissociated and associated states.

Over two-thirds of integral membrane proteins of known structure assemble into oligomers. Yet, the forces that drive the association of these proteins remain to be delineated, as the lipid bilayer is a solvent environment that is both structurally and chemically complex. In this study, we reveal how the lipid solvent defines the dimerization equilibrium of the CLC-ec1 Cl-/H+ antiporter. Integrating experimental and computational approaches, we show that monomers associate to avoid a thinned-membrane defect formed by hydrophobic mismatch at their exposed dimerization interfaces. In this defect, lipids are strongly tilted and less densely packed than in the bulk, with a larger degree of entanglement between opposing leaflets and greater water penetration into the bilayer interior. Dimerization restores the membrane to a near-native state and therefore, appears to be driven by the larger free-energy cost of lipid solvation of the dissociated protomers. Supporting this theory, we demonstrate that addition of short-chain lipids strongly shifts the dimerization equilibrium toward the monomeric state, and show that the cause of this effect is that these lipids preferentially solvate the defect. Importantly, we show that this shift requires only minimal quantities of short-chain lipids, with no measurable impact on either the macroscopic physical state of the membrane or the protein's biological function. Based on these observations, we posit that free-energy differentials for local lipid solvation define membrane-protein association equilibria. With this, we argue that preferential lipid solvation is a plausible cellular mechanism for lipid regulation of oligomerization processes, as it can occur at low concentrations and does not require global changes in membrane properties.

Structure and mechanism of a phage-encoded SAM lyase revises catalytic function of enzyme family.

The first S-adenosyl methionine (SAM) degrading enzyme (SAMase) was discovered in bacteriophage T3, as a counter-defense against the bacterial restriction-modification system, and annotated as a SAM hydrolase forming 5'-methyl-thioadenosine (MTA) and L-homoserine. From environmental phages, we recently discovered three SAMases with barely detectable sequence similarity to T3 SAMase and without homology to proteins of known structure. Here, we present the very first phage SAMase structures, in complex with a substrate analogue and the product MTA. The structure shows a trimer of alpha-beta sandwiches similar to the GlnB-like superfamily, with active sites formed at the trimer interfaces. Quantum-mechanical calculations, thin-layer chromatography, and nuclear magnetic resonance spectroscopy demonstrate that this family of enzymes are not hydrolases but lyases forming MTA and L-homoserine lactone in a unimolecular reaction mechanism. Sequence analysis and in vitro and in vivo mutagenesis support that T3 SAMase belongs to the same structural family and utilizes the same reaction mechanism.

Evolution of Inverted Repeats in Membrane Transporters

The majority of membrane transport proteins of known structure exhibit internal repeat symmetry, with two or more structurally homologous domains. inverted repeat architecture, where the domains are antiparallel with respect to each other, is especially common. This architecture implies the existence of ancestral monomeric proteins that form dual-topology antiparallel dimers, before duplication and fusion led to the extant inverted repeat forms. The Fluc family of fluoride channels is the first family to be discovered that connects these two evolutionary extremes, with both dual topology and inverted repeat representatives. Here we use the Fluc family of fluoride channels to model genetic drift in a newly duplicated membrane protein. Using fluoride resistance assays, we pursue a deep mutational scan to describe how evolutionary forces like mutational robustness and paralogue interference play out after the duplication of single-domain membrane proteins. We connect these experiments to phylogenetic observations of the Fluc superfamily.

Structure of human BCCIP and implications for binding and modification of partner proteins.

BCCIP was isolated based on its interactions with tumor suppressors BRCA2 and p21. Knockdown or knockout of BCCIP causes embryonic lethality in mice. BCCIP deficient cells exhibit impaired cell proliferation and chromosome instability. BCCIP also plays a key role in biogenesis of ribosome 60S subunits. BCCIP is conserved from yeast to humans, but it has no discernible sequence similarity to proteins of known structures. Here we report two crystal structures of an N-terminal truncated human BCCIPβ, consisting of residues 61-314. Structurally BCCIP is similar to GCN5-related acetyltransferases (GNATs) but contains different sequence motifs. Moreover, both acetyl-CoA and substrate-binding grooves are altered in BCCIP. A large 19-residue flap over the putative CoA binding site adopts either an open or closed conformation in BCCIP. The substrate binding groove is significantly reduced in size and is positively charged despite the acidic isoelectric point of BCCIP. BCCIP has potential binding sites for partner proteins and may have enzymatic activity.

MDock: A Suite for Molecular Inverse Docking and Target Prediction.

Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http://zoulab.dalton.missouri.edu/mdock.htm .