Structures In Protein Data Bank Research Articles

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavitiesand cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins(CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank(PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structuresand (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

Abstract 2348: A global multiscale map of protein assemblies from integration of protein interactions and images

Abstract Cells regulate growth and function through a hierarchical structure of subcellular protein assemblies, in which alterations can result in cellular dysfunction leading to disease such as cancer. Much of this structure remains uncharted, resulting in recent efforts to map subcellular organization at different physical scales. Here, we report a global architectural map of human cancer cell protein assemblies spanning 10−9 to 10−5 nm, based on integration of near-proteome-wide affinity purification mass spectrometry-based protein interactions and immunofluorescent imaging in U-2 OS osteosarcoma cancer cells. The U-2 OS multi-scale integrated cell map places >5000 proteins into 270 distinct subcellular protein assemblies across biological scales, representing approximately half of expressed proteins. There are known organelles and protein complexes recovered in the map, as well as 152 putative assemblies, such as a putative interferon signaling complex consisting of a serine protease and STAT transcription factors. The map also incorporates 128 previously uncharacterized proteins into protein assemblies, such as C18orf21 association with a canonical RNAse complex. Protein subsystems in the U-2 OS map were evaluated for the potential for downstream integrative structural modeling, where available structural data (e.g. crosslinking, Protein Data Bank structures, AlphaFold predictions) are combined in a Bayesian framework to model the physical structures of protein complexes. Using this framework, we created an integrated structural model of new proteins interacting with the Rag-Ragulator complex, which regulates MAPK and mTOR signaling. In summary, the global proteome architecture map provides a resource for cellular biology discovery and the systematic determination of protein functions and structures. Citation Format: Leah V. Schaffer, Mengzhou Hu, Edward L. Huttlin, Gege Qian, Abantika Pal, Neelesh Soni, Andrew Latham, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Ignacia Echeverria, Andrej Sali, J Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A global multiscale map of protein assemblies from integration of protein interactions and images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2348.

Synthesis and biological evaluation of 3‐hydroxypyrazoles as aquaporin 9 inhibitors

AbstractA series of 3‐hydroxypyrazole derivatives have been synthesized by a base‐promoted reaction of nitro‐substituted donor–acceptor cyclopropanes with hydrazines. The synthesized compounds have been investigated for their ability to inhibit aquaporin 9 (AQP9) in rat Leydig cells (LC‐540). The protein data bank structure for AQP9 was predicted using homology modeling; and the protein–ligand interaction for the synthesized hydroxyl pyrazole derivatives were analyzed using molecular modeling and docking studies. The results of in silico analyses showed that compound 5b had a higher binding affinity with AQP9 than other compounds. Further, in vitro studies conducted in LC‐540 cells confirmed that compound 5b effectively inhibits AQP9. Hence, compound 5b may be used as an inhibitor in enhancing our understanding of AQP9 function, and in the treatment of several diseases.

Ensemble-based energetic differences between Protein Data Bank structures and AlphaFold2 models

Ensemble-based energetic differences between Protein Data Bank structures and AlphaFold2 models

Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum ( PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl- N’-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of −7.2, −7.1, and −7.0 kcal/mol along with 140296495 were better than the control at −5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

Artificial Intelligence-based database forprediction ofprotein structure andtheir alterations inocular diseases.

The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure prediction. Both Alphafold2 and RoseTTAFold algorithms were built locally using the open-source codebases. A dataset with 48 protein structures from Protein Data Bank (PDB) was adopted for algorithm set-up validation. A website was built to match ocular genes with the corresponding predicted tertiary protein structures for each amino acid sequence. The predicted local distance difference test-Cα (pLDDT) and template modeling (TM) scores of the validation protein structure and the selected ocular genes were evaluated. Molecular dynamics and molecular docking simulations were performed to demonstrate the applications of the predicted structures. For the validation dataset, 70.8% of the predicted protein structures showed pLDDT greater than 90. Compared to the PDB structures, 100% of the AlphaFold2-predicted structures and 97.9% of the RoseTTAFold-predicted structure showed TM score greater than 0.5. Totally, 1329 amino acid sequences of 430 ocular disease-related genes have been predicted, of which 75.9% showed pLDDT greater than 70 for the wildtype sequences and 76.1% for the variant sequences. Small molecule docking and molecular dynamics simulations revealed that the predicted protein structures with higher confidence scores showed similar molecular characteristics with the structures from PDB. We have developed an ocular protein structure database (EyeProdb) for ocular disease, which is released for the public and will facilitate the biological investigations and structure-based drug development for ocular diseases. Database URL: http://eyeprodb.jsiec.org.

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins.

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

Conservation of knotted and slipknotted topology in transmembrane transporters

The existence of nontrivial topology is well accepted in globular proteins but not in membrane proteins. Our comprehensive topological analysis of the Protein Data Bank structures reveals 18 families of transmembrane proteins with nontrivial topology, showing that they constitute a significant number of membrane proteins. Moreover, we found that they comprise one of the largest groups of secondary active transporters. We classified them based on their knotted fingerprint into four groups: three slipknotted and one knotted. Unexpectedly, we found that the same protein can possess two distinct slipknot motifs that correspond to its outward- and inward-open conformational state. Based on the analysis of structures and knotted fingerprints, we show that slipknot topology is directly involved in the conformational transition and substrate transfer. Therefore, entanglement can be used to classify proteins and to find their structure-function relationship. Furthermore, based on the topological analysis of the transmembrane protein structures predicted by AlphaFold, we identified new potentially slipknotted protein families.

CC+ : A Searchable Database of Validated Coiled Coils in PDB Structures and AlphaFold2 Models.

α-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more α helices wrap around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+ , from the RCSB Protein Data Bank (PDB). Here we report an update of CC+ following an update of SOCKET (Socket2) and the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains ≈12,000 coiled-coil assemblies from experimentally determined structures, and ≈120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be updated automatically. We envisage that CC+ could be used to understand coiled-coil assemblies and their sequence-to-structure relationships, and to aid protein design and engineering. This article is protected by copyright. All rights reserved.

Fine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes.

This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. However, the decoy that corresponds to the native structure is not known. We studied 36 nanobody-protein complexes from the single domain antibody database, sd-Ab DB, http://www.sdab-db.ca/. For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of 36 protein data bank (PDB) structures, 25 true structures were predicted as rank 1. Eleven of the remaining structures required Ångstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation, the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the 36 crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.

Using Multiscale Simulations as a Tool to Interpret Equatorial X-ray Fiber Diffraction Patterns from Skeletal Muscle.

Synchrotron small-angle X-ray diffraction is the method of choice for nm-scale structural studies of striated muscle under physiological conditions and on millisecond time scales. The lack of generally applicable computational tools for modeling X-ray diffraction patterns from intact muscles has been a significant barrier to exploiting the full potential of this technique. Here, we report a novel "forward problem" approach using the spatially explicit computational simulation platform MUSICO to predict equatorial small-angle X-ray diffraction patterns and the force output simultaneously from resting and isometrically contracting rat skeletal muscle that can be compared to experimental data. The simulation generates families of thick-thin filament repeating units, each with their individually predicted occupancies of different populations of active and inactive myosin heads that can be used to generate 2D-projected electron density models based on known Protein Data Bank structures. We show how, by adjusting only a few selected parameters, we can achieve a good correspondence between experimental and predicted X-ray intensities. The developments presented here demonstrate the feasibility of combining X-ray diffraction and spatially explicit modeling to form a powerful hypothesis-generating tool that can be used to motivate experiments that can reveal emergent properties of muscle.

Comparative Analysis and Classification of SARS-CoV-2 Spike Protein Structures in PDB

The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of this virus. Analysis and classification of newly determined S protein structures for SARS-CoV-2 are critical to properly understand their functional, evolutionary and architectural relatedness to already known protein structures. In this paper, first, the comparative analysis of SARS-CoV-2 S protein structures is performed. Through comparative analysis, the S protein structures in the PDB (protein data bank) database are compared and analyzed not only with each other but with the structures of other viruses for various parameters. Second, the S protein structures in PDB are classified into different variants, and the associated published literature is studied to investigate what kind of therapeutics (antibodies, T-cell receptors and small molecules) are used on the structures. This is the first study that classifies the S protein structures of the SARS-CoV-2 in PDB into various variants, and the obtained comparative analysis results could be beneficial to the research community, in general, and to crystallographers and health workers, in particular.

PSAC-PDB: Analysis and classification of protein structures

This paper presents a novel framework, called PSAC-PDB, for analyzing and classifying protein structures from the Protein Data Bank (PDB). PSAC-PDB first finds, analyze and identifies protein structures in PDB that are similar to a protein structure of interest using a protein structure comparison tool. Second, the amino acids (AA) sequences of identified protein structures (obtained from PDB), their aligned amino acids (AAA) and aligned secondary structure elements (ASSE) (obtained by structural alignment), and frequent AA (FAA) patterns (discovered by sequential pattern mining), are used for the reliable detection/classification of protein structures. Eleven classifiers are used and their performance is compared using six evaluation metrics. Results show that three classifiers perform well on overall, and that FAA patterns can be used to efficiently classify protein structures in place of providing the whole AA sequences, AAA or ASSE. Furthermore, better classification results are obtained using AAA of protein structures rather than AA sequences. PSAC-PDB also performed better than state-of-the-art approaches for SARS-CoV-2 genome sequences classification.

Alkaloids from Pandanus amaryllifolius Roxb Leaf as Promising Candidates for Antidyslipidemic Agents: An in silico study

Introduction: The plant Pandanus amaryllifolius Roxb (pandan), has been shown to have antidyslipidemic potency. This study explored the potential of several alkaloids from pandan leaf as antidyslipidemia as well as their safety profile in silico. Methods: Analyses were carried out by studying the binding affinity of the alkaloids to 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, peroxisome proliferator activator receptor (PPAR) alpha and Niemann Pick C1 Like 1 (NPC1L1). The structures of the alkaloids were downloaded from the Pubchem database and optimized using the ChemDraw Professional 16.0 to obtain 3D structures in protein data bank (PDB) format. The in silico testing was based on the interactions of the alkaloids with the HMG-CoA reductase (PDB ID 1HW9), PPAR alpha (PDB ID 6LX4) and NPC1L1 (PDB ID 7DFZ) proteins, downloaded from the Research Collaboratory for Structural Bioinformatics (RSCB) PDB website (http://www.rcsb.org/pdb). The preparation of protein structures was performed using the Discovery studio 2021 client and Gromacs applications, while optimization of the 3D structure of the alkaloids was carried out with the ChemDraw professional 16.0. Finally, validation was completed using AutoDock application. The safety profile was assessed by pkCSM online tool. Results: The respective root mean square deviation (RMSD) values of the 1HW9, 6LX4 and 7DFZ proteins were 1.677, 0.918 and 1.706, respectively. The alkaloids pandanusine B, pandamarilactonine A, pandamarilactonine B had respective values of binding energy for HMG-CoA of -5.52, -5.51 and -5.46 kcal/mol. The binding energy of pandamarilactonine B, pandamarilactonine A and pandanamine for PPAR alpha were -9.14, -9.10 and -8.48 kcal/mol, respectively, with the corresponding energy for t NPC1L1 of -9.63, -9.71 and -8.54 kcal/mol. The toxicity tests indicated that the alkaloids were safe, pandamarilactonines had the highest LD50 (2.736 mol/ kg). Conclusion: The studied pandan alkaloids have potential antidyslipidemic activity by interacting with HMG-CoA reductase, PPAR alpha, and NPC1L1, with good safety profile.

Exploring experimental structures and computed structure models from artificial intelligence/machine learning at RCSB Protein Data Bank (RCSB PDB, RCSB.org).

New Artificial Intelligence/Machine Learning (AI/ML) approaches to protein structure prediction have achieved accuracies comparable to low-resolution experimental techniques. Recent breakthroughs (AlphaFold2, RoseTTAFold, etc.) have changed the way Computed Structure Models (CSMs) of proteins are being used across the life sciences. Notwithstanding more than 50,000 structures of human proteins deposited to the global Protein Data Bank (PDB), public-domain experimental coverage of the human proteome is currently only ∼17% of amino acid residues. Including CSMs from AI/ML approaches increases structural coverage of the human proteome to ∼58% of amino acid residues currently defined by experimental methods or confidently predicted by AI/ML software. RCSB PDB recently modified its infrastructure and services to support delivery of CSMs alongside experimentally-determined structures archived in the PDB. At present, the RCSB.org web portal provides open access to ∼200,000 experimentally-determined PDB structures plus >1 million CSMs. Within RCSB.org, CSMs are treated as first-class objects, fully integrated within the RCSB PDB data ecosystem. Users can search, analyze, visualize, compare, and explore PDB structures and CSMs side-by-side.The value of integrating PDB structures and CSMs on RCSB.org is as follows: (1) Many proteins of interest are not represented in the PDB. (2) CSMs provide users with structural information for full-length polypeptide chains. (3) CSMs can accelerate structure determination by 3D electron microscopy and integrative methods. (4) CSMs can be analyzed and visualized more effectively in the context of experimentally-determined PDB structures. RCSB PDB Core Operations are funded by National Science Foundation (DBI-1832184), US Department of Energy (DE-SC0019749), and National Cancer Institute, National Institute of Allergy and Infectious Diseases, and National Institute of General Medical Sciences of the National Institutes of Health under grant R01GM133198.

DAQ-refine: Protein structure model evaluation and refinement for cryo-EM maps.

As cryo-electron microscopy (cryo-EM) has become a more commonly used method in structural biology, many structure models of biomolecules have been determined from cryo-EM density maps. However, our recent research revealed that many protein structure models determined with cryo-EM and deposited in the Protein Data Bank (PDB) have potential errors. Such errors include cases where modeled local conformation is not accurate and situations where misassignment of amino acid types. Thus, establishing a computational protocol that evaluates the model accuracy and corrects low-quality regions in the model is crucial to ensuring the quality of structure models deposited to the public database, PDB. Here, we present a new protocol (DAQ-refine) for evaluating a protein model built from a cryo-EM map and for applying local structure refinement in case the model has potential errors. In the DAQ-refine protocol, model evaluation is performed with a deep learning-based model-local map assessment score, DAQ score, which we developed recently. Then, the subsequent local refinement is performed by a modified procedure of AlphaFold2. In the local refinement step, we used a trimmed template and trimmed multiple sequence alignment as input of AlphaFold2 to control which structure regions to refine while leaving other more confident regions in the model intact. DAQ-refine showed overall the highest accuracy in structure refinement when compared with four other methods on protein structures in PDB that were built from cryo-EM maps determined at a 3-4.5 Å resolution. DAQ score was also able to identify the most accurate model from about 20 alternative models generated for each target protein. The results demonstrated that our protocol, DAQ-refine, consistently improves low-quality regions of initial models.

Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states.

In the efforts to identify structure-function relationships of protein machinery, computational methods have developed rapidly as tools to assist the scientific community in both structural prediction as well as functional relationships in context of biological pathways. Although solving high resolution structural information has been clear for stable folded macromolecules, similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating and analyzing large diverse ensembles of disordered protein states. IDPConformerGenerator builds conformers that obey geometric, steric, and other physical restraints on the input sequence by sampling backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank. All-atom sidechains can be added using robust Monte Carlo algorithms with expanded rotamer libraries. IDPConformerGenerator has many user-configurable options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to provide structural insights into these states that have key biological functions. Ultimately, a better structural understanding of disordered protein ensembles can open up avenues for future research on pharmaceuticals targeting diseases mediated by IDPs.

Efficient protein conformation dynamics characterization enabled by mobility-mass spectrometry

Protein structure dynamics in solution and from solution to gas phase are important but challenging topics. Great efforts and advances have been made especially since the wide application of ion mobility mass spectrometry (IM-MS), by which protein collision cross section (CCS) in gas phase could be measured. Due to the lack of efficient experimental methods, protein structures in protein databank are typically referred as their structures in solution. Although conventional structural biology techniques provide high-resolution protein structures, complicated and stringent processes also limit their applicability under different solvent conditions, thus preventing the capture of protein dynamics in solution. Enabled by the combination of mobility capillary electrophoresis (MCE) and IM-MS, an efficient experimental protocol was developed to characterize protein conformation dynamics in solution and from solution to gas phase. As a first attempt, key factors that affecting protein conformations were distinguished and evaluated separately, including pH, temperature, softness of ionization process, presence and specific location of disulfide bonds. Although similar extent of unfolding could be observed for different proteins, in-depth analysis reveals that pH decrease from 7.0 to 3.0 dominates the unfolding of proteins without disulfide bonds in conventional ESI-MS experiments; while harshness of the ionization process dominates the unfolding of proteins with disulfide bonds. Second, disulfide bonds show capability of preserving protein conformations in acidic solution environments. However, by monitoring protein conformation dynamics and comparing results from different proteins, it is also found that their capability is position dependent. Surprisingly, disulfide bonds did not show the capability of preserving protein conformations during ionization processes.