The PCDDB (Protein Circular Dichroism Data Bank): A Bioinformatics Resource for Protein Characterisations and Methods Development

Machine-learning-guided identification of protein secondary structures using spectral and structural descriptors

Interrogation of the secondary structures of proteins is essential for designing and engineering more effective and safer protein-based biomaterials and other classes of theranostic materials. Protein secondary structures are commonly assessed using circular dichroism spectroscopy, followed by relevant downstream analysis using specialized software. As many proteins have complex secondary structures beyond the typical α-helix and β-sheet configurations, and the derived secondary structural contents are significantly influenced by the selection of software, estimations acquired through conventional methods may be less reliable. Herein, we propose the implementation of a machine-learning-based approach to improve the accuracy and reliability of the classification of protein secondary structures. Specifically, we leverage supervised machine learning to analyze the circular dichroism spectra and relevant attributes of 112 proteins to predict their secondary structures. Based on a range of spectral, structural, and molecular features, we systematically evaluate the predictive performance of numerous supervised classifiers and identify optimal combinations of algorithms with descriptors to achieve highly accurate and precise estimations of protein secondary structures. We anticipate that this work will offer a deeper insight into the development of machine-learning-based approaches to streamline the delineation of protein structures for different biological and biomedical applications.

Biological insights from integrative modeling of intrinsically disordered protein systems

Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they play in the cell. In this short review, we describe a variety of approaches used to obtain biological insight from the structural ensembles of disordered proteins, regions, and complexes and the integrative biology challenges that arise from combining diverse experiments and computational models. Importantly, we highlight findings regarding structural and dynamic characterization of disordered regions involved in binding and phase separation, as well as drug targeting of disordered regions, using a broad framework of integrative modeling approaches.

ChiraKit: an online tool for the analysis of circular dichroism spectroscopy data

Circular dichroism (CD) spectroscopy is an established biophysical technique to study chiral molecules. CD allows investigating conformational changes under varying experimental conditions and has been used to understand secondary structure, folding, and binding of proteins and nucleic acids. Here, we present ChiraKit, a user-friendly, online, and open-source tool to process raw CD data and perform advanced analysis. ChiraKit features include the calculation of protein secondary structure with the SELCON3 and SESCA algorithms, estimation of peptide helicity using the helix-ensemble model, the fitting of thermal/chemical unfolding or user-defined models, and the decomposition of spectra through singular value decomposition or principal component analysis. ChiraKit can be accessed at https://spc.embl-hamburg.de/.

Dark-Field Absorbance Circular Dichroism of Oriented Chiral Thin Films

Dark-field and confocal approaches to circular dichroism (CD) spectroscopy of uniaxial thin films examine the relationship between symmetry and incoherence in the nonreciprocal CD response, or the component that is antisymmetric about the light propagation direction. Modifying a conventional CD spectrometer for low-angle scattering detection isolates incoherent contributions to nonreciprocal CD of drop-cast thin films, boasting 5-to-10-fold enhancements in CD dissymmetry parameters. Conversely, confocal detection suppresses the nonreciprocal CD response. These collective measurements provide the first compelling evidence of early predictions by Hecht and Barron, which indicate large chiral- and interface-specific CD observables from scattered signals in uniaxially oriented assemblies. According to this theory, nonreciprocal CD is possible within the electric dipole approximation, leading to chiral-specific observables exceeding reciprocal, isotropic contributions. Dark-field absorbance CD (DCD) spectroscopy thus offers new insights into molecular and macromolecular arrangements with interface selectivity and chiral specificity.

Analysis of the Structure of 14 Therapeutic Antibodies Using Circular Dichroism Spectroscopy

Understanding the impact of the manufacturing environment on therapeutic monoclonal antibody (mAb) structures requires new process analytical technology. Here, we describe the creation of a new reference set for the circular dichroism (CD) spectra of mAbs. Data sets of the highest quality were collected by synchrotron radiation CD for 14 different mAbs in both native and acid-stressed states. Deconvolution of far-UV spectra for the mAb cohort identified two current reference sets (SP175 and SMP180) as assigning accurate secondary structures, irrespective of the analysis program employed. Scrutiny of spectra revealed significant variation in the far-UV and especially near-UV CD of the 14 mAbs. Two spectral features were found to be sensitive to changes in solution pH, i.e., the far-UV positive peak at 201-202 nm and the near-UV negative exciton couplet around 230-240 nm. The latter feature offers attractive possibilities for in-line CD-based monitoring of the mAb structure during manufacture.

Reference Data Set for Circular Dichroism Spectroscopy Comprised of Validated Intrinsically Disordered Protein Models

Circular dichroism (CD) spectroscopy is an analytical technique that measures the wavelength-dependent differential absorbance of circularly polarized light and is applicable to most biologically important macromolecules, such as proteins, nucleic acids, and carbohydrates. It serves to characterize the secondary structure composition of proteins, including intrinsically disordered proteins, by analyzing their recorded spectra. Several computational tools have been developed to interpret protein CD spectra. These methods have been calibrated and tested mostly on globular proteins with well-defined structures, mainly due to the lack of reliable reference structures for disordered proteins. It is therefore still largely unclear how accurately these computational methods can determine the secondary structure composition of disordered proteins. Here, we provide such a required reference data set consisting of model structural ensembles and matching CD spectra for eight intrinsically disordered proteins. Using this set of data, we have assessed the accuracy of several published CD prediction and secondary structure estimation tools, including our own CD analysis package, SESCA. Our results show that for most of the tested methods, their accuracy for disordered proteins is generally lower than for globular proteins. In contrast, SESCA, which was developed using globular reference proteins, but was designed to be applicable to disordered proteins as well, performs similarly well for both classes of proteins. The new reference data set for disordered proteins should allow for further improvement of all published methods.

An Improved Diabatization Scheme for Computing the Electronic Circular Dichroism of Proteins

We advance the quality of first-principles calculations of protein electronic circular dichroism (CD) through an amelioration of a key deficiency of a previous procedure that involved diabatization of electronic states on the amide chromophore (to obtain interamide couplings) in a β-strand conformation of a diamide. This yields substantially improved calculated far-ultraviolet (far-UV) electronic circular dichroism (CD) spectra for β-sheet conformations. The interamide couplings from the diabatization procedure for 13 secondary structural elements (13 diamide structures) are applied to compute the CD spectra for seven example proteins: myoglobin (α helix), jacalin (β strand), concanavalin A (β type I), elastase (β type II), papain (α + β), 310-helix bundle (310-helix) and snow flea antifreeze protein (polyproline). In all cases, except concanavalin A and papain, the CD spectra computed using the interamide couplings from the diabatization procedure yield improved agreement with experiment with respect to previous first-principles calculations.

Probing the interaction between the metallo-β-lactamase SMB-1 and ampicillin by multispectral approaches combined with molecular dynamics

Metallo-β-lactamases (MβLs) hydrolyze and inactivate β-lactam antibiotics, are a pivotal mechanism conferring resistance against bacterial infections. SMB-1, a novel B3 subclass of MβLs from Serratia marcescens could deactivate almost all β-lactam antibiotics including ampicillin (AMP), which has posed a serious threat to public health. To illuminate the mechanism of recognition and interaction between SMB-1 and AMP, various fluorescence spectroscopy techniques and molecular dynamics simulation were employed. The results of quenching spectroscopy unraveled that AMP could make SMB-1 fluorescence quenching that mechanism was the static quenching; the synchronous and three-dimensional fluorescence spectra validated that the microenvironment and conformation of SMB-1 were altered after interaction with AMP. The molecular dynamics results demonstrated that the whole AMP enters the binding pocket of SMB-1, even though with a relatively bulky R1 side chain. Loop1 and loop2 in SMB-1 undergo significant fluctuations, and α2 (71-73) and local α5 (186-188) were turned into random coils, promoting zinc ion exposure consistent with circular dichroism spectroscopy results. The binding between them was driven by a combination of enthalpy and entropy changes, which was dominated by electrostatic force in agreement with the fluorescence observations. The present study brings structural insights and solid foundations for the design of new substrates for β-lactamases and the development of effective antibiotics that are resistant to superbugs.

KCD: A prediction web server of knowledge-based circular dichroism

We present a web server that predicts the far-UV circular dichroism (CD) spectra of proteins by utilizing their three-dimensional (3D) structures from the Protein Data Bank (PDB). The main algorithm is based on the classical theory of optical activity together with a set of atomic complex polarizabilities, which are obtained from the analysis of a series of synchrotron radiation CD spectra and their related 3D structures from the PDB. The results of our knowledge-based CD method (KCD) are in good agreement with measured spectra that could include the effect of D-amino acids. Our method also delivers some of the most accurate predictions, in comparison with the calculated spectra from well-established models. Specifically, using a metric of closeness based on normalized absolute deviations between experimental and calculated spectra, the mean values for a series of 57 test proteins give the following figures for such models: 0.26 KCD, 0.27 PDBMD2CD, 0.30 SESCA, and 0.47 DichroCalc. From another point of view, it is worth mentioning the remarkable capabilities of the recent approaches based on artificial intelligence, which can precisely predict the native structure of proteins. The structure of proteins, however, is flexible and can be modified by a diversity of environmental factors such as interactions with other molecules, mechanical stresses, variations of temperature, pH, or ionic strength. Experimental CD spectra together with reliable predictions can be utilized to assess eventual secondary structural changes. A similar kind of evaluation can be done for the case of an incomplete protein structure that has been reconstructed by using different approaches. The KCD method can be freely accessed from: https://kcd.cinvestav.mx/.

Best practices for the manual curation of intrinsically disordered proteins in DisProt

The DisProt database is a resource containing manually curated data on experimentally validated intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) from the literature. Developed in 2005, its primary goal was to collect structural and functional information into proteins that lack a fixed three-dimensional structure. Today, DisProt has evolved into a major repository that not only collects experimental data but also contributes to our understanding of the IDPs/IDRs roles in various biological processes, such as autophagy or the life cycle mechanisms in viruses or their involvement in diseases (such as cancer and neurodevelopmental disorders). DisProt offers detailed information on the structural states of IDPs/IDRs, including state transitions, interactions and their functions, all provided as curated annotations. One of the central activities of DisProt is the meticulous curation of experimental data from the literature. For this reason, to ensure that every expert and volunteer curator possesses the requisite knowledge for data evaluation, collection and integration, training courses and curation materials are available. However, biocuration guidelines concur on the importance of developing robust guidelines that not only provide critical information about data consistency but also ensure data acquisition.This guideline aims to provide both biocurators and external users with best practices for manually curating IDPs and IDRs in DisProt. It describes every step of the literature curation process and provides use cases of IDP curation within DisProt. Database URL: https://disprot.org/.

Structure-guided design of VAR2CSA-based immunogens and a cocktail strategy for a placental malaria vaccine

Placental accumulation of Plasmodium falciparum infected erythrocytes results in maternal anemia, low birth weight, and pregnancy loss. The parasite protein VAR2CSA facilitates the accumulation of infected erythrocytes in the placenta through interaction with the host receptor chondroitin sulfate A (CSA). Antibodies that prevent the VAR2CSA-CSA interaction correlate with protection from placental malaria, and VAR2CSA is a high-priority placental malaria vaccine antigen. Here, structure-guided design leveraging the full-length structures of VAR2CSA produced a stable immunogen that retains the critical conserved functional elements of VAR2CSA. The design expressed with a six-fold greater yield than the full-length protein and elicited antibodies that prevent adhesion of infected erythrocytes to CSA. The reduced size and adaptability of the designed immunogen enable efficient production of multiple variants of VAR2CSA for use in a cocktail vaccination strategy to increase the breadth of protection. These designs form strong foundations for the development of potent broadly protective placental malaria vaccines.

PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins

The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.

DichroPipeline: A suite of online and downloadable tools and resources for protein circular dichroism spectroscopic data analyses, interpretations, and their interoperability with other bioinformatics tools and resources

Circular Dichroism (CD) spectroscopy is a widely-used method for characterizing individual protein structures in solutions, membranes, films and macromolecular complexes, as well as for probing macromolecular interactions, conformational changes associated with binding substrates, and in different functionally-related environments. This paper describes a series of related computational and display tools that have been developed over many years to aid in those characterizations and functional interpretations. The new DichroPipeline described herein links a series of format-compatible data processing, analysis, and display tools to enable users to facilely produce the spectra, which can then be made available in the Protein Circular Dichroism Data Bank (https://pcddb.cryst.bbk.ac.uk/) resource, in which the CD spectral and associated metadata for each entry are linked to other structural and functional data bases including the Protein Data Bank (PDB), and the UniProt sequence data base, amongst others. These tools and resources thus provide the basis for a wide range of traceable structural characterizations of soluble, membrane and intrinsically-disordered proteins.

Structural properties of the HNF-1A transactivation domain

Hepatocyte nuclear factor 1α (HNF-1A) is a transcription factor with important gene regulatory roles in pancreatic β-cells. HNF1A gene variants are associated with a monogenic form of diabetes (HNF1A-MODY) or an increased risk for type 2 diabetes. While several pancreatic target genes of HNF-1A have been described, a lack of knowledge regarding the structure-function relationships in HNF-1A prohibits a detailed understanding of HNF-1A-mediated gene transcription, which is important for precision medicine and improved patient care. Therefore, we aimed to characterize the understudied transactivation domain (TAD) of HNF-1A in vitro. We present a bioinformatic approach to dissect the TAD sequence, analyzing protein structure, sequence composition, sequence conservation, and the existence of protein interaction motifs. Moreover, we developed the first protocol for the recombinant expression and purification of the HNF-1A TAD. Small-angle X-ray scattering and synchrotron radiation circular dichroism suggested a disordered conformation for the TAD. Furthermore, we present functional data on HNF-1A undergoing liquid-liquid phase separation, which is in line with in silico predictions and may be of biological relevance for gene transcriptional processes in pancreatic β-cells.

Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions

An unambiguous description of an experiment, and the subsequent biological observation, is vital for accurate data interpretation. Minimum information guidelines define the fundamental complement of data that can support an unambiguous conclusion based on experimental observations. We present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the parameters required for the wider scientific community to understand the findings of an experiment studying the structural properties of intrinsically disordered regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardize the dissemination of the key metadata on an IDR experiment by IDR data sources.

DichroIDP: a method for analyses of intrinsically disordered proteins using circular dichroism spectroscopy

Intrinsically disordered proteins (IDPs) are comprised of significant numbers of residues that form neither helix, sheet, nor any other canonical type of secondary structure. They play important roles in a broad range of biological processes, such as molecular recognition and signalling, largely due to their chameleon-like ability to change structure from unordered when free in solution to ordered when bound to partner molecules. Circular dichroism (CD) spectroscopy is a widely-used method for characterising protein secondary structures, but analyses of IDPs using CD spectroscopy have suffered because the methods and reference datasets used for the empirical determination of secondary structures do not contain adequate representations of unordered structures. This work describes the creation, validation and testing of a standalone Windows-based application, DichroIDP, and a new reference dataset, IDP175, which is suitable for analyses of proteins containing significant amounts of disordered structure. DichroIDP enables secondary structure determinations of IDPs and proteins containing intrinsically disordered regions.

Identification, characterization and in vitro activity of hypoglycemic peptides in whey hydrolysates from rubing cheese by-product

The by-product of Chinese rubing cheese is rich in whey protein. Whey hydrolysates exhibit good hypoglycemic activity, but which specific peptide components are responsible for this effect have not yet been investigated. Herein, the α-glucosidase inhibitory activity of the ultrafiltered fraction (<3 kDa) of rubing cheese whey hydrolysates was evaluated with the inhibition rate of 37.89 %. In addition, peptide identification was conducted using LC-MS/MS, and three peptides YPVEPF, VPYPQ, and LPYPY were identified. Among these, YPVEPF had higher α-glucosidase inhibitory activity (IC₅₀ = 3.52 mg/mL) and interacted with α-glucosidase via hydrogen bonding and hydrophobic forces. YPVEPF was characterized as an amphipathic peptide rich in antiparallel (50.50 %) and random coil (35.20 %) structures, as well as showed good tolerance to gastrointestinal digestion and incubation under the temperature range of 20-80 °C. Notably, YPVEPF activity increased in the presence of Al³⁺ and Fe³⁺, as well as within the pH range of 2.0-6.0. Furthermore, YPVEPF had negligible hemolytic activity at a concentration of 1.0 mg/mL, no toxicity at concentrations below 0.5 mg/mL, and significantly promoted glucose consumption in HepG2 cells (p < 0.0001). Collectively, these findings indicate the potential of YPVEPF to be used as a novel hypoglycemic peptide in functional foods.

MobiDB: 10 years of intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

NACDDB: Nucleic Acid Circular Dichroism Database

The Nucleic Acid Circular Dichroism Database (NACDDB) is a public repository that archives and freely distributes circular dichroism (CD) and synchrotron radiation CD (SRCD) spectral data about nucleic acids, and the associated experimental metadata, structural models, and links to literature. NACDDB covers CD data for various nucleic acid molecules, including DNA, RNA, DNA/RNA hybrids, and various nucleic acid derivatives. The entries are linked to primary sequence and experimental structural data, as well as to the literature. Additionally, for all entries, 3D structure models are provided. All entries undergo expert validation and curation procedures to ensure completeness, consistency, and quality of the data included. The NACDDB is open for submission of the CD data for nucleic acids. NACDDB is available at: https://genesilico.pl/nacddb/.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.