Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

A natural adhesive-based nanomedicine initiates photothermal-directed in situ immunotherapy with durability and maintenance.

Tumor immunotherapies have emerged as a promising frontier in the realm of cancer treatment. However, challenges persist in achieving localized, durable immunostimulation while counteracting the tumor's immunosuppressive environment. Here, we develop a natural mussel foot protein-based nanomedicine with spatiotemporal control for tumor immunotherapy. In this nanomedicine, an immunoadjuvant prodrug and a photosensitizer are integrated, which is driven by their dynamic bonding and non-covalent assembling with the protein carrier. Harnessing the protein carrier's bioadhesion, this nanomedicine achieves a drug co-delivery with spatiotemporal precision, by which it not only promotes tumor photothermal ablation but also broadens tumor antigen repertoire, facilitating in situ immunotherapy with durability and maintenance. This nanomedicine also modulates the tumor microenvironment to overcome immunosuppression, thereby amplifying antitumor responses against tumor progression. Our strategy underscores a mussel foot protein-derived design philosophy of drug delivery aimed at refining combinatorial immunotherapy, offering insights into leveraging natural proteins for cancer treatment.

Role of protein-protein interactions on organization and dynamics of a model chromatin.

The three-dimensional organization of chromatin is influenced by chromatin-binding proteins through both specific and non-specific interactions. However, the roles of chromatin sequence and the interactions between binding proteins in shaping chromatin structure remain elusive. By employing a simple polymer-based model of chromatin that explicitly considers sequence-dependent protein binding and protein-protein interactions, we elucidate a mechanism for chromatin organization. We find that tuning protein-protein interactions and protein concentration is sufficient to either promote or inhibit chromatin compartmentalization. Moreover, chromatin sequence and protein-protein attraction strongly affect the structural and dynamic exponents that describe the spatiotemporal organization of chromatin. Strikingly, our model's predictions for the exponents governing chromatin structure and dynamics successfully capture experimental observations, in sharp contrast to previous chromatin models. Overall, our findings have the potential to reinterpret data obtained from various chromosome conformation capture technologies, laying the groundwork for advancing our understanding of chromatin organization.

CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

Rapid Interpretation of Protein Backbone Rotation Dynamics Directly from Spin Relaxation Data.

Besides their structure, dynamics is pivotal for protein functions, particularly for intrinsically disordered proteins (IDPs) that do not fold into a fixed 3D structure. However, the detection of protein dynamics is difficult for IDPs and other disordered biomolecules. NMR spin relaxation rates are sensitive to the rapid rotations of chemical bonds, but their interpretation is arduous for IDPs or molecular assemblies with a complex dynamic landscape. Here we demonstrate numerically that the dynamics of a wide range of proteins, from short peptides to partially disordered proteins and peptides in micelles, can be characterized by calculating the total effective correlation times of protein backbone N-H bond rotations, τeff, from experimentally measured transverse 15N spin relaxation rates, R2, using a linear relation. Our results enable the determination of magnetic-field-independent and intuitively understandable parameters describing protein dynamics at different regions of the sequence directly from experiments. A practical advance of the approach is demonstrated by analyzing partially disordered proteins in which rotations of disordered regions occur with timescales of 1-2 ns, independent of their size, suggesting that rotations of disordered and folded regions are uncoupled in these proteins.

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.

Biofunctionalized dissolvable hydrogel microbeads enable efficient characterization of native protein complexes.

The characterization of protein complex is vital for unraveling biological mechanisms in various life processes. Despite advancements in biophysical tools, the capture of non-covalent complexes and deciphering of their biochemical composition continue to present challenges for low-input samples. Here we introduce SNAP-MS, a Stationary-phase-dissolvable Native Affinity Purification and Mass Spectrometric characterization strategy. It allows for highly efficient purification and characterization from inputs at the pico-mole level. SNAP-MS replaces traditional elution with matrix dissolving during the recovery of captured targets, enabling the use of high-affinity bait-target pairs and eliminates interstitial voids. The purified intact protein complexes are compatible with native MS, which provides structural information including stoichiometry, topology, and distribution of proteoforms, size variants and interaction states. An algorithm utilizes the bait as a charge remover and mass corrector significantly enhances the accuracy of analyzing heterogeneously glycosylated complexes. With a sample-to-data time as brief as 2 hours, SNAP-MS demonstrates considerable versatility in characterizing native complexes from biological samples, including blood samples.

Greening Separation and Purification of Proteins and Peptides.

The increasing awareness of environmental issues and the transition to green analytical chemistry (GAC) have gained popularity among academia and industry in recent years. One of the principles of GAC is the reduction and replacement of toxic solvents with more sustainable and environmentally friendly ones. This review gives an overview of the advances in applying green solvents as an alternative to the traditional organic solvents for peptide and protein purification and analysis by liquid chromatography (LC) and capillary electrophoresis (CE) methods. The feasibility of using greener LC and CE methods is demonstrated through several application examples; however, there is still plenty of room for new developments to fully realize their potential and to address existing challenges. Thanks to the tunable properties of designer solvents, such as ionic liquids and deep eutectic solvents, and almost infinite possible mixtures of components for their production, it is possible that some new designer solvents could potentially surpass the traditional harmful solvents in the future. Therefore, future research should focus mainly on developing new solvent combinations and enhancing analytical instruments to be able to effectively work with green solvents.

@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

GalaxySagittarius-AF: Predicting Targets for Drug-Like Compounds in the Extended Human 3D Proteome.

In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound. In comparison to its predecessor, GalaxySagittarius, GalaxySagittarius-AF utilizes an enlarged structure database, incorporating curated AlphaFold model structures alongside their binding sites and ligands, predicted using an updated version of GalaxySite. GalaxySagittarius-AF covers a large human protein space compared to many other available computational target screening methods. The structure-based prediction method enhances the use of expanded structural information, differentiating it from other target prediction servers that rely on ligand-based methods. Additionally, the web server has undergone enhancements, operating two to three times faster than its predecessor. The updated report page provides comprehensive information on the sequence and structure of the predicted protein targets. GalaxySagittarius-AF is accessible at https://galaxy.seoklab.org/sagittarius_af without the need for registration.

xiVIEW: Visualisation of Crosslinking Mass Spectrometry Data.

Crosslinking mass spectrometry (MS) has emerged as an important technique for elucidating the in-solution structures of protein complexes and the topology of protein-protein interaction networks. However, the expanding user community lacked an integrated visualisation tool that helped them make use of the crosslinking data for investigating biological mechanisms. We addressed this need by developing xiVIEW, a web-based application designed to streamline crosslinking MS data analysis, which we present here. xiVIEW provides a user-friendly interface for accessing coordinated views of mass spectrometric data, network visualisation, annotations extracted from trusted repositories like UniProtKB, and available 3D structures. In accordance with recent recommendations from the crosslinking MS community, xiVIEW (i) provides a standards compliant parser to improve data integration and (ii) offers accessible visualisation tools. By promoting the adoption of standard file formats and providing a comprehensive visualisation platform, xiVIEW empowers both experimentalists and modellers alike to pursue their respective research interests. We anticipate that xiVIEW will advance crosslinking MS-inspired research, and facilitate broader and more effective investigations into complex biological systems.

E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence.

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

BACKGROUND: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing. RESULTS: Here, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it's also useful to analyze interactions in other types of protein complexes.

Diffusion of proteins in crowded solutions studied by docking-based modeling.

The diffusion of proteins is significantly affected by macromolecular crowding. Molecular simulations accounting for protein interactions at atomic resolution are useful for characterizing the diffusion patterns in crowded environments. We present a comprehensive analysis of protein diffusion under different crowding conditions based on our recent docking-based approach simulating an intracellular crowded environment by sampling the intermolecular energy landscape using the Markov Chain Monte Carlo protocol. The procedure was extensively benchmarked, and the results are in very good agreement with the available experimental and theoretical data. The translational and rotational diffusion rates were determined for different types of proteins under crowding conditions in a broad range of concentrations. A protein system representing most abundant protein types in the E. coli cytoplasm was simulated, as well as large systems of other proteins of varying sizes in heterogeneous and self-crowding solutions. Dynamics of individual proteins was analyzed as a function of concentration and different diffusion rates in homogeneous and heterogeneous crowding. Smaller proteins diffused faster in heterogeneous crowding of larger molecules, compared to their diffusion in the self-crowded solution. Larger proteins displayed the opposite behavior, diffusing faster in the self-crowded solution. The results show the predictive power of our structure-based simulation approach for long timescales of cell-size systems at atomic resolution.

Maximal Genetic Code Symmetry Is a Physicochemical Purine-Pyrimidine Symmetry Language for Transcription and Translation in the Flow of Genetic Information from DNA to Proteins.

Until now, research has not taken into consideration the physicochemical purine-pyrimidine symmetries of the genetic code in the transcription and translation processes of proteinogenesis. Our Supersymmetry Genetic Code table, developed in 2022, is common and unique for all RNA and DNA living species. Its basic structure is a purine-pyrimidine symmetry net with double mirror symmetry. Accordingly, the symmetry of the genetic code directly shows its organisation based on the principle of nucleotide Watson-Crick and codon-anticodon pairing. The maximal purine-pyrimidine symmetries of codons show that each codon has a strictly defined and unchangeable position within the genetic code. We discovered that the physicochemical symmetries of the genetic code play a fundamental role in recognising and differentiating codons from mRNA and the anticodon tRNA and aminoacyl-tRNA synthetases in the transcription and translation processes. These symmetries also support the wobble hypothesis with non-Watson-Crick pairing interactions between the translation process from mRNA to tRNA. The Supersymmetry Genetic Code table shows a specific arrangement of the second base of codons, according to which it is possible that an anticodon from tRNA recognises whether a codon from mRNA belongs to an amino acid with two or four codons, which is very important in the purposeful use of the wobble pairing process. Therefore, we show that canonical and wobble pairings essentially do not lead to misreading and errors during translation, and we point out the role of physicochemical purine-pyrimidine symmetries in decreasing disorder according to error minimisation and preserving the integrity of biological processes during proteinogenesis.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

Aromatic Residues in Proteins: Re-Evaluating the Geometry and Energetics of π-π, Cation-π, and CH-π Interactions.

Aromatic residues can participate in various biomolecular interactions, such as π-π, cation-π, and CH-π interactions, which are essential for protein structure and function. Here, we re-evaluate the geometry and energetics of these interactions using quantum mechanical (QM) calculations, focusing on pairwise interactions involving the aromatic amino acids Phe, Tyr, and Trp and the cationic amino acids Arg and Lys. Our findings reveal that π-π interactions, while energetically favorable, are less abundant in structured proteins than commonly assumed and are often overshadowed by previously underappreciated, yet prevalent, CH-π interactions. Cation-π interactions, particularly those involving Arg, show strong binding energies and a specific geometric preference toward stacked conformations, despite the global QM minimum, suggesting that a rather perpendicular T-shape conformation should be more favorable. Our results support a more nuanced understanding of protein stabilization via interactions involving aromatic residues. On the one hand, our results challenge the traditional emphasis on π-π interactions in structured proteins by showing that CH-π and cation-π interactions contribute significantly to their structure. On the other hand, π-π interactions appear to be key stabilizers in solvated regions and thus may be particularly important to the stabilization of intrinsically disordered proteins.

bio2Byte Tools deployment as a Python package and Galaxy tool to predict protein biophysical properties.

SUMMARY: We introduce a unified Python package for the prediction of protein biophysical properties, streamlining previous tools developed by the Bio2Byte research group. This suite facilitates comprehensive assessments of protein characteristics, incorporating predictors for backbone and sidechain dynamics, local secondary structure propensities, early folding, long disorder, beta-sheet aggregation, and fused in sarcoma (FUS)-like phase separation. Our package significantly eases the integration and execution of these tools, enhancing accessibility for both computational and experimental researchers. AVAILABILITY AND IMPLEMENTATION: The suite is available on the Python Package Index (PyPI): https://pypi.org/project/b2bTools/ and Bioconda: https://bioconda.github.io/recipes/b2btools/README.html for Linux and macOS systems, with Docker images hosted on Biocontainers: https://quay.io/repository/biocontainers/b2btools?tab=tags&tag=latest and Docker Hub: https://hub.docker.com/u/bio2byte. Online deployments are available on Galaxy Europe: https://usegalaxy.eu/root?tool_id=b2btools_single_sequence and our online server: https://bio2byte.be/b2btools/. The source code can be found at https://bitbucket.org/bio2byte/b2btools_releases.