Here, we present Machaon, a data-driven method combining orientation invariant metrics on phi-psi angles, inter-residue contacts and surface complexity. It can be readily applied on whole structures or segments—such as domains and binding sites. Machaon was applied on SARS-CoV-2 Spike monomers of native, Delta and Omicron variants and identified correlations with a wide range of viral proteins from close to distant taxonomy ranks, as well as host proteins, such as ACE2 receptor. Machaon’s meta-analysis of the results highlights structural, chemical and transcriptional similarities between the Spike monomer and human proteins, indicating a multi-level viral mimicry. This extended analysis also revealed relationships of the Spike protein with biological processes such as ubiquitination and angiogenesis and highlighted different patterns in virus attachment among the studied variants. Available at:  The presented method Machaon identifies similar whole structures or fragments with SARS-COV-2 Spike using alignment-free metrics on phi-psi angles, inter-residue contacts and surface complexity and enriches these relationships via meta-analysis. Protein tertiary structure can be up to ten times more conserved than genomic sequence5 and can reveal a greater genomic similarity. Currently, access to protein structures unprecedentedly increased due to advances in experimental and in silico approaches, such as refined Cryo-EM6 and AlphaFold7. This progress stresses the need for improved methodologies that can efficiently identify structurally similar proteins beyond distinct domains or common families and origins. Such approaches could connect structure to molecular and cellular function and/or genomic information with structural and chemical properties. These methods can also be applied to assess structural mimicry mechanisms employed by viruses to control host systems8 or to quickly functionalize proteins of newly discovered viruses or variants, supporting biomarker discovery, drug design or repurposing.Protein structure strongly dictates function, with minimal changes often dramatically affecting major properties9, rely on high sequence similarity, which is often inadequate for revealing distant relationships. Established structure-based approaches, such as the Dali server10 and Research Collaboratory for Structural Bioinformatics Protein Data Bank’s (RCSB PDB)11 search based on BioZernike12, emphasize the high rate of structural similarity with fixed thresholds. From a performance perspective, most standardized metrics for structural comparison13, such as the Template Modeling Score (TM-Score)14, require prior superposition of whole structures, which is a computationally intensive task. It is becoming apparent from the above that there is an unmet need for new methods to perform structural comparisons.The currently available methods span a wide gamut of approaches, focusing on primary, secondary, or tertiary protein structure. Sequence-based search methods, such as BLAST15. However, to our knowledge, no existing method combines features such as torsion angles17, residue distances19 or representations like alpha shapes20 to a unified framework. In this study, we present Machaon, a methodology that relies on hypothesis-free clustering and ranking proteins by concurrently calculating and utilizing comparative protein differences of torsional angles (B-phipsi), inter-residue distances (W-rdist), and surface complexity . These metrics are not linked with the length or the orientation of a structure, and alignments are utilized solely in constrained comparisons to prune the search space21. Machaon not only identifies structurally similar proteins to a reference protein but also performs meta-analysis, building a profile with extended comparisons and analysis of the results. The meta-analysis module assesses the genomic or transcriptomic sequence and protein 1D/2D/3D/chemical structural similarities. It also proposes evolutionary relationships, indicating functions of the reference protein, by highlighting areas of its secondary structure.Structure comparison by multiple criteria has been previously proposed, such as pyMCPSC22 and affects the cell compositions and biological pathways of vital organs23. It carries transmembrane trimeric spike glycoproteins (S protein) on its envelope, binding to the Angiotensin-Converting Enzyme 2 (ACE2) receptor on the cellular membrane, allowing cell entry24. Machaon enabled us to efficiently and accurately identify structurally similar proteins from different proteomes by comparing the monomer of the S protein with three large datasets: a viral PDB dataset, a human PDB dataset and a predicted human proteome by AlphaFold. We extended our comparisons with the viral dataset on the domain level for a more fine-grained search. We also combined the metrics with mixed representation alignments for an in-depth constrained search to find structurally similar protein segments to binding sites. The accompanied meta-analysis allowed us to form hypotheses on distant evolutionary paths, potential functional structural motifs and associations between functions or pathways and the reference protein. Finally, we were able to investigate structural differences between the native, Delta and Omicron Spike monomer variants by comparing Machaon’s results for each monomer on the viral dataset.As a proof-of-concept study, we applied Machaon on the Spike protein monomer of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in both its native form and its Delta and Omicron variants. SARS-CoV-2 virus greatly impacts the host respiratory system causing excessive inflammation responseMachaon is an in silico analysis suite that identifies and selects the most structurally similar proteins from a user-specified pool of candidates, having a protein of interest as a reference Fig. . This op18. We measure 3D similarity with TM-Score25, an established metric that is independent of protein length, and 2D sequence identity from 2D fold sequence alignments (see “Methods: Enrichment and assessment of the selected candidate entries”). For validation purposes, the results are pseudo-labeled based on the specific criteria of each task.To validate the method, we appraised the accuracy and limitations of Machaon on tasks of whole structure comparisons on different cases and datasets Table . We retr26  family utilizing BioZernike’s validation set. This dataset is a non-redundant set of 2685 structures belonging to 151 CATH families. We employed Machaon for a random protein per family in the set  and a series of comparisons between every protein represented more than once and the rest of the proteins in the set  2.07 from a snapshot of the entire PDB (2018). The provided dataset consisted merely of ATOM records, so we kept just the first conformation in each file for compatibility (1867 PDB files were edited). We also excluded the d2zjq51/q105A domain that contained only the coordinates of alpha-carbons (no angle information available), updating the ground truth table. Fmax scores were computed with the benchmark’s publicly available code for the untruncated final cluster per query domain, evaluating the ranking of the proteins in the results against the target SCOP classification  of SCOP140 benchmarkon Table .29, Delta30 and Omicron31) and a dataset of ~12,500 PDB files containing ~40,000 structures (median resolution: 2.64 Å) belonging mostly to proteomes of viruses 37 (related to Hypoxia) and Aminopeptidase N (ANPEP)39 but also molecules without reported association such as Cleavage and Polyadenylation Specific Factor 1 (CPSF1), which is involved in a T cell pathway40. All mentioned proteins share structural similarities with Spike monomer in the 2D and 3D level as well as chemical and/or genomic/transcriptomic similarities—in 5’-end/3’-end Untranslated Regions  and coding regions (CDS)  and the viral dataset revealed several viral proteins belonging to the family of Coronaviridae in the top similarity results. However, viruses with great taxonomic distance, such as Human Immunodeficiency and Dengueens Fig.  also appFollowing global comparisons, we conducted in-depth comparisons of domain and binding sites, using the native Spike monomer as a reference. We employed Machaon to identify proteins with structurally similar segments to the S1 N-terminal (NTD), S1 C-terminal (CTD), S1 receptor-binding (RBD) domains and to pre-computed binding sites, wherever it was applicable and possible by the available data. The results include a broader range of viral families, both distant and close, compared to those obtained from whole structure comparisons, like Papillomaviruses and Ebolaviruses 48, Thyroglobulin (TG)49, contactin-associated protein-like 3 (CTNNAP3)50, Hypoxia up-regulated protein 1 (HYOU1)51 are top entries with reported relationships to the virus. The method again ranks members of alpha-2-macroglobulin/complement 3 protein family (A2M/C3)52 in high order: pregnancy zone protein (PZP)53, CD109 antigen and Alpha-2-macroglobulin54, which appears in the results of both human protein datasets. The selected candidates from these datasets share similarities in protein 2D/3D/chemical structure and transcript sequence, as observed in the results of the viral dataset 56, a process that has been reported to be vital for virus replication57 . The measurements suggested that most of these versions would have been identified if they were present as unknown proteins in the whole structure comparisons results for Spike and viral dataset. The median values of the metric vector [9 or structure63  and how this is reflected in the resulting surface . Machaon perceives angles and residue distances as unstructured data and emphasizes the shape of their respective distributions. B-phipsi and W-rdist metrics quantify the structural similarity of the two compared proteins according to the same occurrence of phi-psi angle pair or inter-residue values in their peptidic chains. T-alpha serves as a geometrical filter and efficiently quantifies surface complexity in a smoothened unit that is decoupled in size. The feature selection leans toward the side of the secondary protein structure, and this is verified by the results. Each of the features has the convenient property of being human-interpretable and carries meaningful information. The method attempts to reduce the structures in dimension, complexity and proportion, bringing them to the same scale and smoothing out minor differences . To our knowledge, Machaon is the first structural comparison methodology that combines these three features under two different settings: whole structure and mixed-alignment-informed segment scanning. This is linked with meta-analysis that can offer hints for biological properties via multiple secondary structure alignments. This module also interfaces with various well-established data sources and methods, enriching further the resulting output with extended comparisons to genomic, proteomic and chemical levels.The results are derived from the structures available in the PDB file. Thus, it is possible that some protein structures might be incomplete and partially represented, a common fundamental obstacle found in these datasets. Data preparation is a labor-intensive and resource-intensive process that could insert biases. Through Machaon, many of the above-mentioned limitations are overcome due to the fundamental principles of the methodology. More specifically, Machaon analyzes the experimental data in their raw form without any preprocessing or refinement, such as treating missing residues. Also, each protein may participate with several structures in the dataset due to the incompleteness of the available data, as two different PDBs of the same protein might contain different parts of the structure in different conformations and resolutions. This is highly desired since the ideal dataset for this kind of task would comprise information on different resolutions, conformations, protonation states, isoforms, etc. The metrics appear to be robust against a degree of noise, as demonstrated by the high overlap observed between the results for the raw and preprocessed version of the Spike monomer.25. The presented results confirm that the method operates beyond domains or folding classes and retrieves similar structures in a wider scope, balancing between secondary and tertiary structure similarities. Machaon’s constrained searches offer fine-grained scanning with a manageable performance cost, identifying alternative conformations of the same protein that have very low TM-Scores. This particular case attests to the difficulty of structural search since there is no perfect solution or indisputable ground truth. An alternative conformation of the same protein having a TM-Score lower than 0.5 would be overlooked in favor of a protein from the nearest CATH or SCOP class64. Machaon is not relying on prior knowledge or hard cutoffs and does not discriminate candidates during scanning. Its data-driven manner, combined with its fuzzy metrics, is suitable for the investigation of challenging cases such as the novel Spike protein of SARS-CoV-2.We thoroughly tested Machaon on public datasets and three individual tasks. The median TM-Score for whole structure comparisons in all tasks was well above 0.17, accounting for a meaningful relationship between a pair of compared structures65 and lack a defined 3D structure. The power and interest in such approaches became obvious with the appearance of the AlphaFold model7. The latter models distributions of dihedral angles and paired residue distances to predict the secondary structure, an intermediate step for the prediction of the tertiary structure. Our proposed method could be used with predicted data, such as the Human Proteome data from AlphaFold DB66 or the metagenomic proteins from ESM Metagenomics Atlas67 and decipher previously unknown relationships. Machaon’s metric values extend to the set of real positive numbers and close to zero, which are attractive traits for their potential adoption as objective functions in Machine Learning tasks. The suggested metrics could also be included ad hoc into Virtual Screening pipelines or provide known targets in drug repurposing studies by finding similar sites belonging to proteins that have been previously validated using ligand-binding studies.Identifying proteins with common folds could reveal distant evolutionary relationships or common traits from convergence evolution since structure is more conserved than sequence. This, in turn, provides further justification or indication about existing or novel common functions, cellular locations, protein interactions, structural motifs and pathways. The knowledge of structurally similar proteins could form hypotheses for intrinsically disordered proteins (IDPs) that belong to the dark proteome70 regulatory relationships like transcription factors, miRNAs and immune system evasion or regulatory mechanisms73. For instance, it was recently reported that the SARS-CoV-2 virus hijacks host processes related to ubiquitination and that Spike protein participates in such a process74. These findings are in accordance with our indications of a potential Spike-ubiquitination relationship which could be researched further as a possible case of antagonism in the immune system’s responses71.We used Machaon to identify proteins that have structural similarities with SARS-CoV-2 Spike monomer. The results on the viral dataset further validate the accuracy of the method since many proteins with a high order belong to the Coronaviridae family. The generated viral taxonomy trees could initiate a further investigation of SARS-CoV-2’s distant evolutionary paths. Judging from the lower median values of the gap included identities concerning the domain scanning, it is most possible that some of the identified domain similarities would have been missed in a global sequence or a maximum structural alignment context. The method ignores any sequence or structural gaps and considers similarity in a manner of local feature alignment. Therefore, it is suitable for finding less obvious, intermediate structural relationships, probably revealing valuable genomic similarity information or potential functional motifs, as in the case of the segment-scanning between Spike monomer and ACE2. Furthermore, the extended comparisons on sequence, structural and chemical levels reveal more areas of relevance. The results for the viral and human datasets demonstrate such multi-level similarities between the Spike monomer and human proteins. This could hint at linked expression patterns with host genes, sharing or hijackingIn brief, we investigated three structural metrics and combined them to establish a comparative analysis method. Machaon was shown to effectively encompass the suggested measurements in the context of global and segmented comparisons seeking structurally similar proteins. The proposed method was applied to investigate the SARS-CoV-2 Spike protein uncovering prominent similarities with different sets, including viral and host proteins. Some of the revealed proteins had previously documented relationships, further validating this technique. However, there were also novel findings involving the interactions or pathways of the proteins in the host organism that require further investigation.Conclusively, we demonstrated that Machaon is a powerful approach that operates beyond conventional structural comparative methods. As structural comparison appears to provide more information than linear sequence comparison, it will be interesting to extend Machaon to other molecular structures apart from proteins, including DNA and RNA. Using an innovative comparative structure method, in addition to the conventional primary sequence alignments and an advanced meta-analysis tool, will provide a fresh angle of view and a powerful new approach for molecular studies.75 of phi-psi angles (b-phipsi) is a metric based on the multivariate distribution distance DB between two sets of phi-psi angle pairs Bhattacharyya distance76 between two distributions Wasserstein inter-residue contacts distance (w-rdist) focuses on intra-molecular paired residue distances. This metric is the log-normalized Wasserstein (or Earth’s Mover) distancea atoms in the protein backbone are taken into account in b-phipsi and w-rdist for efficiency and robustness.Only the C77, which engulfs a point cloud from the normalized atomic coordinates of a protein. The value of the alpha parameter for the computation of t-alpha is set to 0.085 based on manual tuning on a set of homologous protein structures in different resolutions. The number of triangles  is a metric that relies on the number of triangles found by Delaunay Triangulation that cover an alpha shape78 and RCSB PDB data. These data include the range of the residue positions of each present domain in a candidate protein structure. Each of the domains is compared with the ones of the reference protein in separate paired sessions. The results contain information on the compared domains.The protein domain comparisons rely on available domain information from UniProt79. The spatial outliers are discarded based on the interquartile range (IQR). Hydropathy Index80 is grouped into distinct classes of neighboring values  and a hydrophobicity label to an ASCII character for both reference and candidate proteins. Separate local alignments between the contiguous reference segment parts and the candidate’s sequence define the areas on which the metrics are going to be computed . Overlapping aligned ranges are unified through an iterative process, eliminating any falsely over-represented intersecting areas. The final results also include full information on the alignment of each candidate protein for further inspection. Alternatively, there is an option for the user to choose 1D, 2D or Hydrophobicity sequence alignments instead.This mode requires the segments of interest as input. The positions of the reference protein’s interacting residues are converted into multiple contiguous residue ranges by Gaussian Mixture Models (GMM) clustering supported by Silhouette Analysis29, 3D0H.B83):Protein primary structure sequences:GDSTEJSNLLLQYGSFITQLNRALTGIAVEQ, SNSTEAGQKLFNMLRLGKSEPWTLALEN.Sequence of hydrophobicity group labels:3433463411143332034145213302044, 3433423441242151343433312144.Protein secondary structure sequences:TT.HHHHHHHGGGGGGTHHHHHHHHHHHHHH, TT.HHHHHHHHHHHTTTTSS.HHHHHHH.The alignment of mixed representations (ASCII), mapping hydropathy cluster labels to secondary structure states:Below there is an example of the mixed sequence alignment between a part of a Spike protein’s predicted binding site (positions 744–774) and ACE2 (positions 545–572) based on PDB data (6VXX.ANO}$%’$%""7:9998 K$%"%&#"-------$$!#!%%|||||.|| ||..|| ||.|.||NO}$%#$%----------%"#%#"PLNOUV}$$"#"%%84 (min_samples = 5 and Euclidean distance metric)  and user-specified constraints. Depending on the dataset’s total size, 1% of the top rows is sampled for each metric and combined into a new set with duplicates removed. The samples are ordered by the Borda Count rank aggregation algorithm and are clustered by HDBSCAN clustering algorithm85 and EBI QuickGo86 online web services. Duplicate protein or gene entries are discarded, keeping the entry with the highest order by the metrics. An evaluation module performs global sequence alignments, determines the sequence identity, computes the 3D similarity with a modified version of TM-Align and calculates chemical similarity for each of the proteins in the final sample. The alignments  take place between 1D/2D partial data derived from PDBs (leaving a gap between non-consecutive residues), full 1D sequences from UniProt (matches/mismatching scoring by BLOSUM62 matrix87) and RNA sequences from NCBI RefSeq88. The sequence identity is calculated both with gaps excluded and included and unless stated otherwise, the sequence identity metric displayed in the rest of the article is the one with the gaps included:The resulting list of the selected proteins is enriched with information from UniProt’s ID mapping offline resource and from RCSB PDB GraphQL, UniProt, NCBI EntrezFinally, these computations are appended to the results, and the combined output, along with aggregated information on Gene Ontology terms, is exported to a UniProt data-enriched, printer-friendly HTML report by a separate presentation module. This module also generates lineage trees, word cloud  is calculated with a span equal to 30% of the reference protein sequence length. The peaks  with PropKa91 and minimized with OPLS force field92  was manually processed via the Schrodinger Maestro Suite with the Protein Preparation WizardVXX was mFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting Summary


Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at:   Biomolecular interactions are crucial in cellular environments. In particular, proteins mediate a wide range of molecular processes through their interactions. Progress in computation has paved the way to a better understanding of the role of structure and dynamics in defining the function of these biomolecular systems. All these advances can only be understood with the plethora of experimentally determined structures at atomic resolution deposited at the Protein Data Bank  (PDB) anLikely, computational docking might still help in filling this gap and get further insights into molecular association. These classical approaches aim at building three-dimensional models of macromolecular structures by first generating thousands of possible conformations and then discriminating between biologically- and non-biologically relevant poses. With the World Wide Web and all the advances in web software development, it is essential to provide user-friendly web-based services  to make https://server.lightdock.org/), an entirely rewritten version of the LightDock framework in the Rust programming language for optimal speed and performance.LightDock is a fully open-source framework for flexible protein–protein, protein–peptide and protein–DNA docking, based on a swarm intelligence optimization algorithm: Glowworm Swarm Optimization (GSO) . Swarm ihttps://github.com/lightdock/lightdock-rust, open source released under GPLv3 license), the LightDock Server is capable of running several jobs in parallel with minimum memory footprint.The server backend has been developed using the Flask Python web micro-framework to process user jobs and to bridge with the LightDock software. The user interface is developed in HTML5 (rendered by Jinja2 template engine) and with NGL Viewer  componenIn the following sections, we report the implementation of the server and describe its potential and applicability by showcasing the modeling of protein-protein interactions under different scenarios,DNA’ for the receptor partner. In the third and final step, users may enable or disable backbone flexibility for any docking partner. Note that for membrane-associated receptors, flexibility is disabled.The LightDock Server is free to use and does not require user registration for any of its predictive modes. The landing page Figure  providesOnce a job is submitted, the user is redirected to a newly generated view, which is unique for the job and must be bookmarked. This view is regularly updated to provide feedback on the status of the job and, once the simulation is completed Figure , will diWorkspace, an effective view with four main sections  All Jobs shows all jobs submitted by the user, sorted by submission date and with pagination support, (iii) Active Jobs shows queued and running jobs submitted by the user and in process by the server and (iv) Outdated Jobs view shows jobs submitted by the user with a finish date older than 14 days. Besides the job history, the main advantage of these three last views is the ability to perform special actions on the jobs being processed (stopping them at any point) or on the ones already completed (removing them from the server).Additionally, registering is possible if users desire to keep track of their jobs for future reference. Once they have registered, users have access to their profile section  and to the s Figure : (i) NewWe have designed dedicated modeling modes depending on the molecule's type and on the absence/presence of information to drive the modeling. Before describing each of them in detail in the following sections, here we provide a number of general considerations. The LightDock Server, which builds upon the LightDock framework, offers the possibility of modeling protein–protein, protein–peptide and protein–DNA interactions. Specifically, and to improve user experience, nine (9) different scenarios are supported Figure  in termsProtein–proteinProtein–protein + DNAProtein–DNAProtein + DNA–proteinDNA–proteinMembrane protein–proteinAntibody–proteinAntibody–protein + DNAAntibody–DNAswarms based on the size and shape of the receptor. However, users may increase the number of initial swarms for a denser sampling. When there is information about the putative interface between two molecules, the LightDock Server can incorporate it in the form of residue restraints. These restraints can be specified on the receptor or the receptor and ligand. Please note that restraints on the ligand only are not allowed since this will waste computational resources (note: switch the order of the molecules). If specified on the receptor, the initial swarms will be filtered out, and irrelevant sampling regions will be excluded prior to the simulation. If also specified on the ligand, the initial ligand poses will be oriented based on randomly selected receptor-ligand restraint pairs. Besides sampling, the restrained satisfaction rate will bias the scoring during optimization. Once the simulation is completed, the resulting models will be filtered according to the defined restraints, thus assuring that the delivered models agree with the data provided.When there is no information about the putative interface between two molecules, an exhaustive exploratory docking simulation is an alternative. By default, LightDock generates a dynamic number of Help > Minimization section online) using OpenMM  to, given an anchor residue, generate an approximated explicit bead membrane mimicking a lipid layer. This preprocessing will enable our membrane-based protocol and the topological information encoded in the membrane will be incorporated into the modeling. It is crucial that the structure provided is in the right orientation so that the generated swarms will be placed in the right position. Additionally, the user could define extra information in both molecules to further narrow the optimization process. As in all restraints-guided protocols, the docked models will be filtered out according to the information provided, with the possibility of a final relaxation.Membrane protein systems have historically been one of the most difficult systems to study in structural biology. As we have shown, using membrane-derived topological information when modeling the interaction between transmembrane proteins and their soluble partners significantly impacts docking performance. When there is no information about the position of the membrane in the MemProtMD database , the usehttps://server.lightdock.org/examplesIn this section, we provide a step-by-step guide for modeling a protein-peptide interaction, an antibody-antigen complex, and a membrane-associated protein assembly for demonstrative purposes. All the input files and LightDock results are available at: 1czy_receptor.pdb) and the 7-residue long sequence of the LMP1 peptide  in an extended conformation. Assuming prior knowledge of two interfacial residues on the receptor, we will select A.411 and A.466 as residue restraints with default parameters, flexibility on both molecules and final relaxation of the top 10 models. As shown in Figure Tumor necrosis receptor-associated factors (TRAFs) can turn on numerous genes involved in inflammatory and immune responses and sustain proliferation during tumorigenesis. TRAFs are constitutively recruited by binding an apparent wide spectrum of peptidic sequences, such as the oncoprotein LMP1, for which the crystal structure was solved over 20 years ago (PDB code: 1czy). For this demonstration, we will use the unbound structure of the receptor from the PeptiDB database (3mxw_receptor.pdb) as a receptor and select Antibody as molecule type. The Shh protein (3mxw_ligand.pdb) will be therefore considered as the ligand (and Protein molecule type). In the next step, we will see the residues pertaining to the CDR loops as defined by the numbering scheme highlighted in pink for double-checking. At this step, the user might also introduce extra information such as residue restraints, but we will leave those boxes empty for this demonstration and the remaining parameters as default. Once the simulation finishes, the best five models according to the score will be displayed, and as we can see in Figure The neutralizing capacities of antibodies are beyond established. However, these proteins have proven key for characterizing molecular mechanisms in biological systems. For example, antibodies can be used for trapping multi-passing transmembrane proteins in distinct conformations to get insights into their functioning. In this example, we will model the interaction between the anti-Shh 5E1 antibody and the Sonic Hedgehog metalloprotease (Shh), a transmembrane protein crucial in cellular differentiation during embryogenesis (PDB code: 3mxw). First, it is important to note that for activating the dedicated antibody mode in the LightDock Server, the antibody must be numbered according to the Chothia scheme and must have chain ids H and L for the heavy and light chains respectively. We will then use this structure (Claudins are multipass membrane proteins with a major role in cell adhesion. The Clostridium perfringens enterotoxin (C-CPE) can bind to certain claudins, triggering tight junction disintegration and increasing permeability across epithelial cell sheets. It is reasonable to assume that the C-CPE enterotoxin will bind to the extracellular domain(s) of Claudin-19 to disrupt these cell–cell connections. In this example, we will model the Claudin-19–C-CPE (PDB code: 3x29) interaction using membrane topological information from the MemProtMD database, thus targeting the sampling towards the extracellular region.Data Download section and download the PDB file corresponding to the coarse-grained (CG) snapshot (MARTINI representation). This file in PDB format contains the MARTINI CG representation of the phospholipid bilayer together with the protein complex. Since we will make use of the phosphate beads as the boundary for the transmembrane region for filtering the sampling region of interest in LightDock, we will remove all atoms except for those representing the PO4 bead and rename them to BJ with MMB residue name (3x29_receptor_membrane.pdb). Given that LightDock performs all-atom docking calculations, we must replace the CG protein with the atomistic one . Next, we will retrieve the PDB file of the ligand from the MemCplxDB database (3x29_ligand.pdb). To activate the membrane mode, select Membrane protein as the molecule type for the receptor, Protein for the ligand and enable flexibility on the latter. In this mode, the dynamically generated initial swarms will be filtered to be compatible with the definition of the membrane. As shown in Figure First, we will browse the 3x29 complex page at MemProtMD, locate the https://www.uniprot.org/uniprot/Q9ET38) and our in-house Membrane Builder utility to build a CG bead layer embedding Claudin-19. The topological information from the UniProt entry indicates that it contains four helical transmembrane regions  and five extramembranous regions: three cytoplasmic  and two extracellular (segments 29–81 and 139–160). Based on this information, we must select an anchor residue (e.g. S138) and default parameters for execution.Alternatively, when the information is unavailable in the MemProtMD database, we can use sequence-derived information from UniProt  in the absence (blind) or presence (driven) of experimental information. Since the original implementation of the LightDock framework, the algorithm showed comparable performance to state-of-the-art docking software , with inhttps://lightdock.org/tutorials/0.9.3/rna_docking) or the rational prediction of PROTAC-compatible protein-protein interfaces . Validation of the software against numerous docking benchmarks as well as comparisons against other algorithms can be found in references 27, 28, 29, 30 and 31, whose results are publicly available and have been also linked at https://server.lightdock.org/help#benchmarking_and_references.The LightDock software is freely available to academic and commercial users (10.5281/zenodo.7706358), including the faster implementation in the Rust programming language (10.5281/zenodo.5883405). LightDock is also distributed via the Python Package Index (


In recent years, brewer’s spent grain (BSG) has gained attention as a plant-based protein source because it occurs in large quantities as a by-product of beer brewing. BSG can contribute to future food requirements and support the development of a circular economy. In light of the dynamic developments in this area, this review aims to understand the proteins present in BSG, and the effect of extraction techniques and conditions on the composition, physicochemical, and techno-functional properties of the obtained protein extracts. The water-insoluble hordeins and glutelins form the major protein fractions in BSG. Depending on the beer brewing process, the extraction technique, and conditions, the BSG protein isolates predominantly contain B, C, and ϒ hordeins, and exhibit a broad molecular weight distribution ranging between <5 kDa and >250 kDa. While the BSG isolates obtained through chemical extraction methods seem promising to obtain gelled food products, physical and enzymatic modifications of BSG proteins through ultrasound and proteolytic hydrolysis offer an effective way to produce soluble and functional protein isolates with good emulsifying and foaming capabilities. Specifically tailored protein extracts to suit different applications can thus be obtained from BSG, highlighting that it is a highly valuable protein source. The valorization of sidestreams from plant food processing plays a vital role in today’s day and age when plant proteins have gained considerable attention to foster the transition towards a more sustainable protein supply for the growing population. Brewer’s spent grain (BSG) is the sidestream that remains after the wort production during the beer brewing process. While the generated wort is utilized for the fermentation process, the BSG is not used any further in the brewing process. BSG accounts for around 85% of the total sidestreams generated by the brewing industry, with 20 kg of it being produced for every 100 L of beer . BSG canThe type, content, and properties of proteins present in barley, the principal ingredient used for beer production, are shown in BSG is mainly comprised hordeins and glutelins , with albumins and globulins only being present at low concentrations (7.5%) . The strThe market for plant-based proteins has been very dynamic in recent years, with researchers and product developers constantly striving to discover novel, highly functional alternative protein sources. BSG has the potential to be used as a highly viable plant protein source providing possible economic, nutritional, and sustainability benefits ,33. The As wet BSG obtained after the brewing process contains high amounts of moisture (75–80%); it is first dried to enhance the shelf life, reduce the volume, and ease material handling . After dThis section reviews different extraction methods and the variations in the processing parameters in each of the extraction methods. Extraction yield and protein purity have been taken as measures to compare the effectiveness of various extraction procedures, with the former being defined as protein recovery based on BSG protein content, and the latter being the protein content in the isolated protein.A set of different pretreatments are often used before extraction, to assist protein release from the BSG matrix. Defatting, particle size reduction through milling or shearing, enzymatic hydrolyzation, and ultrasound are the most common pretreatments, with the latter two also being used independently as ‘chemical free’ alternatives to traditional alkaline extraction  37,38,3,47,48,54Additionally, it is important to note that differences in the raw materials and processing parameters during brewing might alter BSG composition and, consequently, the protein extraction. While this information is lacking in most of the studies in the literature, a study by Connolly et al.  highlighAlkaline extraction is a well-studied method to recover proteins from different kinds of agricultural sources, including BSG. In this method, the alkaline conditions facilitate protein solubilization by altering protein structure, charge, and, consequently, interactions ,47,48,58The key variables determining the effectiveness of the extraction process include the type of alkali used as well as its concentration, the extraction temperature, the solid: liquid ratio, and the isoelectric point used for protein precipitation. While sodium hydroxide has been the most commonly employed alkali to obtain alkaline conditions during the production of BSG protein isolates, its concentration in the solution is known to have a profound influence on extraction yield and purity. For instance, a ~twofold increase in the extraction yield was achieved when the sodium hydroxide concentration was increased from 0.05 to 0.15 M and from 0.01 to 0.1 M ,46. Simiw/v) [Changing the solid-to-liquid ratio resulted in a maximum extraction yield of proteins at a ratio of 1:20 (4.8% w/v) .Researchers utilized a wide range of pH values ranging from pH 2 to 4.5 to precipitate the proteins. This is due to the presence of different proteins found in BSG that are known to have isoelectric points ranging from pH 6 (hordeins) to around pH 3.7–4.5 (glutelins) ,60.Increasing the number of extraction cycles also influenced the protein yield. Qin et al. , for insOther extraction parameters have also been investigated and their influence on protein yield and purity was described:The consistently low protein purities of 37 to 69% across various studies indicates that efficient fractionation procedures are required to separate proteins from the high amounts of fiber present in BSG to obtain a high protein purity. Future work in this direction, coupled with efforts to reduce the use of alkaline reagents and water volume, is needed to extract proteins sustainably.BSG is a complex food matrix that contains proteins entrapped within complex carbohydrate structures. Extraction methods using acids and reducing agents, such as sulfuric acid and sodium bisulfite, aim to hydrolyze the hemicellulose present in the cell walls of raw materials, and break the disulfide bonds in proteins, increasing their solubility and facilitating their recovery . Moreovew/w of dry BSG. This was attributed to the inability of reducing agents to dissociate the proteins from the cellulosic material present in BSG. The study recommended that the addition of detergents such as sodium dodecyl sulfate (SDS) during the extraction could enhance the protein solubility. However, when utilizing acidification and reducing agents it has to be kept in mind that these compounds need to fulfill all regulatory aspects applicable to food use.Aiming to determine the optimal processing conditions, different concentrations of acids and reducing agents have been evaluated. Qin et al.  reportedSalt extraction combined with surfactants, using SDS and disodium phosphate (DSP), promotes the solubilization of the protein aggregates of BSG . ExtractThe two solvent extraction techniques that have been evaluated to isolate proteins from BSG include the use of deep eutectic solvents and pressurized solvent extraction. Deep eutectic solvents can fractionate and dissolve lignin and starch compounds in the raw material, facilitating the release of proteins . High teFirst, after evaluating different deep eutectic solvents, Wahlström et al.  found thIn the terms of industrial scalability, pressurized solvent extraction in a biorefinery is feasible when considering the process itself, the equipment needed, and the costs required . The useHydrothermal extraction uses water as a solvent at mild to high temperatures for short or long periods to facilitate the release of proteins . SubcritSimilar or better extraction yields and protein purities have been obtained through these extraction methods compared to alkaline and acid-based extractions , indicatHydrolysis of BSG with enzymes such as carbohydrases and proteases aims to facilitate protein solubilization through the breakdown of carbohydrates and proteins resulting in the formation of protein hydrolysates ,53,55. AAspergillus niger, and Promod144 papain, from papaya fruit, work at pH 3.5 and 6.5, respectively. The mixtures are incubated at 50 to 60 °C for 3 to 5 h (depending on the enzyme used) and the solubilized proteins are recovered through centrifugation [The addition of carbohydrases and proteases can either be carried out as a one-stage process or separately as a two-stage process ,53,55. Ifugation ,55, dryifugation ,44,54.The enzyme concentration, pH of the mixture, and incubation time were identified as the main factors critical to achieving high extraction yields. In general, an increase in the enzyme load led to higher extraction yields. Yu et al. , for insThe incubation time is another important parameter that has been reported to affect the extraction yield. In the presence of Alcalase at 10 μL per gram of dry BSG, Yu et al.  observedThe main challenge in combined treatments of carbohydrases and proteases is the low purity of the extracts at ~40%. The reason for this is that carbohydrases solubilize small molecular weight sugars from BSG. Microfiltration of the liquid extracts after enzymatic treatments could separate sugars from the proteins and thereby increase their purity. For instance, Frederix and Greden  obtainedLow protein purity in the final extract with an average of ~50% remained a common challenge across various extraction methods studied. As discussed before, this is typically related to the hydrolysis and solubilization of carbohydrates. Adding processing operations such as centrifugation or membrane filtration to separate these compounds from the solubilized proteins seems to be a viable solution to increase protein purity. Additionally, the combination of different extraction methods could also help to achieve a synergistic effect on the yield and purity of proteins derived from BSG. Mixed results have, however, been reported, with a wide range of protein yields (43 to 86%) and purities (58–65%) being obtained through cascade extraction methods .Multi-step extraction approaches such as acid−alkaline and enzyme−alkaline techniques did not necessarily demonstrate a positive effect on extraction yield. For example, while an increase in protein recovery from ~50 to 65% was observed upon adding an acid pretreatment prior to alkaline extraction , cellulaUltrasound technology has recently been studied as a method to enhance the solubilization of proteins prior to or during alkaline or enzymatic extraction by the formation and collapse of bubbles and sonolysis of water ,42,46,54The use of microwaves, like ultrasound, during alkaline extraction also helps to enhance protein recovery. Aiming to induce intracellular heating and disruption of the cell walls of BSG, Barrios et al.  applied On a final note, while alkaline extraction is currently the most established method of protein extraction , chemicaThe compositional and structural properties of the extracted BSG proteins vary with the raw materials used, the brewing process, and the extraction methods and conditions. A broad molecular weight distribution ranging from <5 kDa to >250 kDa has therefore been reported in the literature for BSG proteins, with most extracts being dominant in B, C, and ϒ hordeins .Although barley has traditionally been used to produce beer, a variety of starch-rich materials including other cereals, pseudo-cereals, or vegetables are currently being used in conjunction with barley. The type and content of proteins present in BSG, therefore, differ significantly. For instance, BSG obtained from cereals like barley and wheat is expected to be naturally rich in prolamins (43–80%), which is in contrast to oats and rice, where globulins (70–80%) and glutelins 64–74%) form the major storage proteins ,63,64,65–74% formThe processes of malting, mashing, and kilning involved in the brewing process considerably influence the barley protein profile when it is transformed to BSG. Celus et al.  studied The extent of heating, either during kilning at temperatures of up to 200 °C after malting, or during wet protein extraction, is known to cause protein denaturation and aggregation. For instance, Kumari et al.  reportedThe extraction techniques and their conditions further influence the composition and structure of the obtained protein fractions . Ervin ePhysicochemical properties such as solubility, denaturation temperature, charge, interfacial activity, and others are important characteristics of proteins to determine their functional behavior in food matrices. Some of these properties have been determined for proteins found in BSG and will be discussed below.Protein solubility is the main physicochemical property that determines the suitability of proteins to be used in different food formulations at the required environmental conditions. Depending on the conditions , proteins need to be selected that remain soluble or undergo a solubility transition. For example, liquid foods typically require a protein that remains soluble, whereas proteins often form a three-dimensional network by controlled desolubilization and aggregation in solid foods. Thus, it is important to understand protein solubility under different environmental conditions.Several authors have investigated the solubility of the proteins found in BSG under different conditions. In general, BSG proteins have a very low solubility in the pH range that is relevant for food applications (pH 2 to 9) ,37. CeluBecause of the low solubility of BSG proteins, several authors studied the effect of protein extraction and hydrolysis on the solubility of proteins found in BSG ,48,71 T. Here, tCelus et al.  establisTd of 125 °C and a glass transition temperature Tg of 60.05 °C for a dry powder [Td = 163.5 °C [The thermal stability of proteins is important during many food processing operations. The unfolding of proteins affects the ability of proteins to remain in solution or to form gels. Thus, it is important to understand at which temperature the proteins found in BSG denature. This is commonly measured using differential scanning calorimetry, but the available data on the thermal stability of BSG proteins are very limited. Most studies investigated glass melting/denaturation temperatures of the protein fractions that are also present in BSG in a dry state, such as hordein. One study investigated the denaturation temperatures of a barley protein extract that was mainly comprised the hordein and glutelin fraction (which is similar to BSG). The authors revealed a denaturation temperature y powder . Other ay powder ,85,86 ha163.5 °C . It has 163.5 °C .Interestingly, one study investigated the denaturation behavior of a protein isolate obtained from BSG at a concentration of 5% but no peaks were detected in the temperature range of 20–120 °C . AccordiSurface hydrophobicity characterizes the exposed hydrophobic residues in a protein and is a function of the protein structure and amino acid composition. Limited studies have characterized this property as a function of extraction methods and conditions. A recent study found that the surface hydrophobicity of BSG proteins increased with increasing extraction temperature and pH during alkaline extraction, probably owing to protein unfolding and denaturation under these conditions . InteresTechno-functional properties that derive from the physicochemical properties are important to form different food structures, such as emulsions, foams, and gels. In this section, we will review the ability of proteins found in BSG to stabilize such food matrices.Higher water and oil holding capacities are desirable in viscous and/or gelled food product applications like mayonnaise, yogurt, and plant-based meat applications. While water retention helps to reduce moisture loss during cooking, specifically in plant-based meat applications, the presence of fat enhances the mouthfeel and helps to retain the flavors in food products .w/w. Similarly, Li et al. [In general, BSG proteins exhibit better WACs (3–5 g/g)  comparedi et al. , who use® , a commercial BSG protein isolate produced via enzymatic hydrolysis, was lower compared to alkaline and ultrasound-assisted alkaline extracts  and emulsion stability index (ESI) by Li et al.  and NegiRhizopus oligosporus [Thus, untreated BSG showed rather inferior emulsifying properties compared to other protein materials, and for this reason, other authors investigated the emulsifying properties of hydrolyzed BSG proteins . In mostgosporus . Howevergosporus ,72,76,892/g (BSG) to 78 m2/g (extracted BSG) to 110 m2/g (extracted BSG treated with flavourzyme to a degree of hydrolysis of 2%) [0 > 10 × 105 [For example, untreated BSG exhibited an overall low EAI, which was significantly increased by an alkaline extraction step and even further increased by hydrolysis. Especially a treatment with flavourzyme was able to increase the EAI from around 22 ms of 2%) . Interess of 2%)  or ESIs s of 2%)  at incre10 × 105 .In conclusion, the emulsifying properties of BSG are considerably changed upon hydrolysis and are influenced by the utilized enzyme. Depending on the obtained degree of hydrolysis, the formation and stability of the emulsion might be either increased or decreased. Especially limited hydrolysis with Flavourzyme and pepsin seem to be efficient in facilitating emulsion formation and stabilization, respectively.anti-foaming agent with almost no foaming ability [The ability of an ingredient to stabilize the air−liquid interface is another important functional property. Especially proteins are well known for their ability to form and stabilize foams  and ther ability ,91.Li et al.  treated Proaño et al.  revealedCelus et al. ,89, NaibMore recent studies that draw attention to the proteins in BSG reveal that the proteins extracted from BSG via the alkaline extraction route possess rather low foaming properties with especially low foam stabilization (<7 s) capabilities in the acidic pH range  but highIn conclusion, foam formation and stability are considerably enhanced upon protein extraction from BSG and can be further tuned by hydrolysis, but the type of protease and degree of hydrolysis considerably affects the resulting functional property.The ability to stabilize solid and semisolid food structures is an additional important functionality that determines the formation of structures such as gelled and extruded food products. Indeed, BSG and BSG protein extracts have been investigated as potential ingredients to structure solid food matrices. Most studies investigated the potential of using BSG in extruded food products, whereas a limited number of studies investigated how the proteins found in BSG could be used as a gelation agent.The comprehensive study of Hellebois et al.  revealedBSG was utilized as an ingredient during extrusion processing to produce puffed snacks, pasta, breakfast cereals, different nutritional ingredients, and bread ,102,103.For expanded snack products, the addition of BSG causes a decrease in product expansion after the die, with a more dense macrostructure formed . For exaLactiplantibacillus plantarum PU1 the sensory profile of BSG enriched (15%) pasta could be considerably enhanced. However, not all oral processing parameters—such as chewiness—could be mitigated and especially color was significantly altered towards more brown in all formulations [BSG was also incorporated during cold extrusion processes, such as pasta production. When micronized (barley) BSG is added to semolina-based pasta only small amounts (5–10% depending on BSG origin) could be added until the sensory scores were negatively affected ,104. Forulations . ConsequSimilar to pasta, a recent study compared the suitability of two different fractions obtained from BSG for bread making, namely a protein-rich and a fiber-rich fraction . The resLastly, BSG was added to other solid foods to partly replace other ingredients to increase the fiber and protein content as well as to fortify the products with compounds that possess antioxidative properties such as ferulic and phenolic acids ,110. IndOverall, fortification of different solid foods with BSG seems to be a promising tool to add more fiber and protein to different formulations, but the sensorial and functional properties are typically modified upon addition, which often limits the amount that can be added to the food.w/w) of the total amino acids. This is in line with these being the main amino acids of barley hordeins [w/w) of the total protein [w/w) have been reported in BSG protein extracts for each of the S-containing amino acids methionine and cysteine [The amino acid profile along with the protein digestibility act as the two key determinants of the nutritive value of a protein. Glutamine and proline form the most abundant amino acids in BSG protein extracts, constituting 30–38.5%  when an alcalase mediated extraction was used compared to alkaline extraction . Further, the type of enzyme used during extraction also impacts the overall amino acid profile. Krissa et al. [He et al.  studied a et al. , for insThe impact of different pretreatments was reported by Qin et al. , who shoWhile the use of different deep eutectic solvents (DES) did not alter the overall amino acid profile, lower lysine contents in the protein extracts and residues were obtained after DES treatment compared to raw BSG. This could potentially be related to lysine degradation or fragmentation into smaller molecular weight peptides that could not be retained during the dialysis step .Further, the extraction methods and conditions are known to influence the type and content of amino acids present in the BSG protein extracts:In terms of digestibility, barley protein has the highest protein digestibility corrected amino acid score (PDCAAS) value of 0.55, compared to other commonly used grains in beer production, including wheat, corn, and rice, having values of 0.41, 0.43, and 0.47 respectively . The PDC® has around 85% protein and is most likely obtained by a combination of enzymatic hydrolysis to break down the starch and proteins followed by centrifugal separation and micro- as well as nanofiltration steps to purify the resulting proteins [BSG has been extensively studied and several potential applications have been proposed over the years, which have been recently reviewed by Chetrariu and Dabija . Howeverproteins . Accordi®  sell BSG-enriched flour mixes for brownies, pizza, bread, and cakes. In addition, their portfolio contains pasta, puffs, and bars containing their BSG product. The company patented a BSG processing operation based on intermittent infrared drying combined with stirring that yields a final product with a lower microbial load, increased crispiness, and a more pleasant flavor [Other companies such as Regrainedt flavor .While the current industrial applications of BSG protein isolate are limited, its appreciable water and oil holding capacity , coupledThe large amount of BSG produced makes it a promising material for food applications. Research efforts over the last years has shed more light on the optimization of extraction methods, the protein composition, and the functionality of the proteins found in BSG. Enzymatic treatments and fractionation methods seem to be promising techniques to obtain functional protein extracts that can be coupled with alkaline extraction methods. Solubility, emulsifying, and foaming properties can be increased by hydrolysis approaches, whereas untreated or fractionated BSG can be used in solid foods such as pasta, bread, and cookies. A major drawback of the current state of knowledge is that many studies do not report the raw material composition of BSG, which limits some fundamental understanding of its properties. Future research towards the incorporation of these proteins in high-protein plant-based products and their bioavailability in these matrices would further aid in expanding their utilization on a commercial level.


Toxoplasma gondii (T. gondii) is one of the most successful parasites in the world, because about a third of the world's population is seropositive for toxoplasmosis. Treatment regimens for toxoplasmosis have remained unchanged for the past 20 years, and no new drugs have been introduced to the market recently. This study, performed molecular docking to identify interactions of FDA-approved drugs with essential residues in the active site of proteins of T. gondii Dihydrofolate Reductase (TgDHFR), Prolyl-tRNA Synthetase (TgPRS), and Calcium-Dependent Protein Kinase 1 (TgCDPK1). Each protein was docked with 2100 FDA-approved drugs using AutoDock Vina. Also, the Pharmit software was used to generate pharmacophore models based on the TgDHFR complexed with TRC-2533, TgPRS in complex with halofuginone, and TgCDPK1 in complex with a bumped kinase inhibitor, RM-1–132. Molecular dynamics (MD) simulation was also performed for 100 ns to verify the stability of interaction in drug–protein complexes. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis evaluated the binding energy of selected complexes. Ezetimibe, Raloxifene, Sulfasalazine, Triamterene, and Zafirlukast drugs against the TgDHFR protein, Cromolyn, Cefexim, and Lactulose drugs against the TgPRS protein, and Pentaprazole, Betamethasone, and Bromocriptine drugs against TgCDPK1 protein showed the best results. These drugs had the lowest energy-based docking scores and also stable interactions based on MD analyses with TgDHFR, TgPRS, and TgCDPK1 drug targets that can be introduced as possible drugs for laboratory investigations to treat T. gondii parasite infection. Toxoplasma gondii is an obligate intracellular protozoan parasite with a polar apical complex and an attachment to the host cell membrane2. Toxoplasma gondii infects almost all mammals and birds, including humans, domestic animals, and wild animals. In some infected people, mild symptoms like an influenza-like illness, such as muscle aches and swollen lymph nodes, appear for weeks or months3. Eye problems are also seen in a few patients. Some patients with weakened immune systems may develop neurological problems such as imbalance and coordination of muscles and organs4. The acute form of the disease may be associated with fever and lymphocytosis. In the rare acute form of the disease, symptoms of pneumonia, general muscle involvement, and even death occur5. Cats are the definitive host for T. gondii. Humans and other mammals act as intermediate hosts. Drugs used to treat toxoplasmosis have toxic side effects and need long periods of time, ranging from weeks to more than a year. The need for long-term treatment and the risk of recurrence of the disease is partly due to inefficiency against T. gondii tissue cysts. Challenges to creating a more effective treatment for toxoplasmosis include reducing toxicity, achieving therapeutic concentrations on the brain and eyes, shortening the duration, removing tissue cysts from the host, safety in pregnancy, and developing a formula that is available in inexpensive and practical sources6. Drug therapies affect the reproductive stage of tachyzoite by inhibiting folic acid biosynthesis and protein synthesis. For decades, drug regimens for human toxoplasmosis have included sulfadiazine, pyrimethamine, and autocon, which are effective against other parasites, such as Plasmodium. Other drugs include spiramycin, azithromycin, and clindamycin. Trimethoprim and sulfamethoxazole both effectively combat the parasite, so cotrimoxazole, a combination of the two, is the preferred treatment of 3–4 weeks and is usually prescribed with folic acid to reduce the drug's side effects. Failure to treat existing medications may be due to long-term use. However, due to the complex life cycle of the parasite, the development of resistance, and treatment failure in Toxoplasma chorioretinitis, encephalitis, and congenital infections is increasing and needs further investigation. The duration of initial treatment for Toxoplasmic encephalitis is at least six weeks, followed by secondary suppression until adequate recovery of the immune system. Long-term treatment is partly because current clinical drugs cannot eliminate the cystic stage of T. gondii. Drug development and administration are essential with a reliable understanding of the mechanism of action, the specific site of the target, and the consequences of its behavior on the host and the parasite7. Currently, approved drugs cannot clear chronic infections in the human body, so approximately one-third of the world's human population is at risk of reactivation, with potentially severe consequences. Drugs that have been successfully repurposed in research, along with the ever-increasing costs and failures of traditional drug discovery, have led to the emergence of a new field of drug repurposing8. Nowadays, scientists are trying to replace drugs with fewer and milder side effects with old drugs that do not respond to the treatment of some diseases or have high side effects9.T. gondii parasite, as calcium ionophores can stimulate microneme secretion and suppress some activities11. Calcium-dependent protein kinase 1 (CDPKs) is a family of serine/threonine kinases. Replication of T. gondii depends on its ability to invade host cells, which is partially mediated by CDPK1. TgCDPK1 is an essential regulator of calcium-dependent exocytosis in this opportunistic human pathogen. Suppression of this protein has been shown to control the calcium-dependent secretion of organelles called micronemes, thereby blocking essential phenotypes such as parasite motility, host cell invasion, and egress14. In some studies, TgCDPK1 is a potential new target in T. gondii for treating this infection16. Recent studies also have targeted parasite enzymes that are involved in protein synthesis. These enzymes play an essential role in proteostasis19. Prolyl-tRNA synthetases (PRS) are among the enzymes that have received much attention. These enzymes charge tRNA molecules with L-pro for protein translation. In recent studies, parasite-encoded PRSs have been extensively evaluated using febrifugein and its derivatives, such as halofuginone20. Apicomplexan parasites use the combined enzyme thymidylate synthase-Dihydrofolate Reductase (TS-DHFR) for folate metabolism and pyrimidine biosynthesis, which are essential for cell growth and proliferation. TS-DHFR is an attractive target for pharmacological interventions. Drugs that inhibit this drug target with their competitive inhibitory properties are widely used in the clinic to treat parasitic infections, including toxoplasmosis23. In this study, we have developed a computational approach to analyze the inhibitory effect of some promising drugs against T. gondii CDPK1, PRS, and DHFR proteins using molecular docking evaluation and MD simulation, where we can select useful drugs from all the investigated drugs. For this aim, we examined the interaction of 2100 FDA-approved drugs with three T. gondii CDPK1, PRS, and DHFR proteins individually. Finally, we identified the most important drugs with inhibitory effects on different proteins of T. gondii.Studies suggest that calcium signaling pathways are important in T. gondii DHFR (PDB ID: 6AOH) were investigated. Evaluation analysis of molecular docking results were scored based on the formation of H-bonds, the affinity scores, the interaction energies, and interaction with the amino acid residues present in the binding site of TgDHFR protein. We analyzed the docking scores predicted by AutoDock Vina and ranked the top compounds in the first level of docking results screening. Compounds with low affinity scores were first rejected and not further analyzed. Drugs with high side effects such as anti-cancer drugs as well as expensive drugs were excluded from our drug list. Then, thoroughly analyzed and presented the interactions between the best compounds based on the docking results and the residues inside the protein binding pocket in Table https://www.rcsb.org/structure/6AOH). The 3D structural views of the ligand–protein interactions are provided in Fig. The binding energies of the selected FDA-approved drugs with the https://www.rcsb.org/structure/5XIQ)  are provided in Table 24. Betamethasone and Bromocriptin, as effective drugs against TgCDPK1 protein (with a structure similar to RM-1–132 compound) were also detected by the Pharmit server based on pharmacophore calculations (Table The binding energies of the selected FDA-approved drugs with the TgCDPK1 protein PDB ID: 3SX9) were studied. The results of drug interactions with the protein were ranked based on affinity scores, interaction energies, number of bonds, and interaction of ligands with amino acid residues in the active site of the protein. In the first screening stage, we relied on the docking scores predicted by AutoDock Vina and ranked the top compounds. Drugs with lower affinity and more positive energy were first rejected and not further analyzed. The results of the interactions between the drugs and the residues inside the active site and the RMSD score related to the virtual screening of the Pharmit server, were thoroughly analyzed and presented in Table SX9 were MD is a computer simulation technique that investigates the physical motions of atoms and molecules. Evaluating the stability of the docked complexes Root Mean Square Fluctuation (RMSF), Root-mean-square Distance (RMSD), hydrogen bonds (H-bonds), radius of gyration (RG), and solvent-accessible surface area (SASA) were calculated. In MD simulation, atoms and molecules are allowed to interact for a fixed period. It provide a view of the dynamic evolution of the system and visualize the effect of ligand binding on protein structural changes. The highest estimated binding energy and the best orientation of the ligand in the active site were selected to perform MD simulations. In the interaction with the DHFR protein, drugs of Ezetimibe, Phenazopyridine, Raloxifene, Sulfasalazine, Triamterene, Zafirlukast, and DHFR-TRC-2533 complex were chosen for MD simulation.RM-1–132-CDPK1, Omeprazole-CDPK1, Pantoprazole-CDPK1, Betamethasone-CDPK1, Bromocriptine-CDPK1 complexes were chosen to check the stability of drugs in the interaction with the CDPK1 protein. Halofuginone-PRS, Cromolyn-PRS, Ergotamine-PRS, Montelukast-PRS, Cefexim-PRS, and Lactulose-PRS complexes were also evaluated to analyze the stability of complexes.The RMSD results for drugs interacting with the TgDHFR protein showed that all drugs eventually reached stability Fig. A. The av2, respectively.Molecular dynamics simulation was performed for PRS-Halofuginone, Cromolyn-PRS, Ergotamine-PRS, Montelukast-PRS, Cefexim-PRS, and Lactulose-PRS complexes up to 100 ns. The RMSD results showed that all the complexes were stable, with low fluctuations during the simulation Fig. A. HalofuThe average SASA value of our selected complexes based on molecular docking and Halofuginone-PRS complex as the control was close , and all complexes were stable during the simulation Fig. D. In add2 respectively , and binding energies of complexes were calculated by the MMPBSA method. SASA of the complexes was calculated to predict the extent of conformational change during the interaction. As shown in Table T. gondii is a ubiquitous intracellular parasite that infects humans and warm-blooded animals25. The rapid spread of drug-resistant parasites has led scientists to identify and use vulnerable points in T. gondii parasites to discover alternative drugs and eradicate or control infections associated with this parasite27. Therefore, the discovering new antiparasitic compounds and new targets for treatment is essential28. The development of strategies to target the key vulnerabilities in eukaryotic parasites is essential to identify new therapies against these infections. To abandon the difficult and expensive ways of classical drug discovery, targeted drug discovery on common and conserved pathogenic targets is increasing29. Considering the high costs and time-consuming nature of new drug discovery and development, reuse of old drugs for the treatment of common and rare diseases is increasingly becoming an attractive proposition, due to the use of compounds with lower side effects and overall costs and shorter drug development timelines30. Various data-driven and experimental approaches have been suggested to identify of repurposable drug candidates. The regulatory and phase III costs may remain more or less the same for a repurposed drug as for a new drug in the same indication, but there could still be substantial savings in preclinical and phase I and II costs31. Together, these advantages with lower average associated costs once failures can result in a less risky and more rapid return on investment in the development of repurposed drugs. In this study, to find an alternative treatment instead of common treatments against T. gondii infection, we examined the interaction of 2100 approved drugs with three TgDHFR, PRS, and CDPK1 proteins. Previous studies have shown that these proteins are suitable targets for the vulnerability and elimination of T. gondii parasite23. The results of our computational studies using molecular docking showed drugs with an inhibitory effects against each protein. Then, we performed molecular dynamics and MMPBSA analyses for more accurate evaluations. A competent inhibitor of the TgDHFR protein should have sufficient selectivity to inhibit parasiticidal activity without simultaneously inhibiting human DHFR (hDHFR). Selective drugs for TgDHFR protein should have a higher affinity for TgDHFR than hDHFR32. Since we chose 5-(4-(3-(2-methoxypyrimidin-5-yl) phenyl) piperazin-1-yl) pyrimidine-2, 4-diamine- TgDHFR complex as control. In this complex, the compound 5-(4-(3-(2-methoxypyrimidin-5-yl) phenyl) piperazin-1-yl) pyrimidine-2, 4-diamine binds to the TgDHFR with a higher affinity than the hDHFR one32. As a result, we chose drugs that have stronger interaction with TgDHFR compared to the 5-(4-(3-(2-methoxypyrimidin-5-yl) phenyl) piperazin-1-yl) pyrimidine-2, 4-diamine compound. Finally, Ezetimibe, Raloxifene, Sulfasalazine, Triamterene, and Zafirlukast drugs showed the best results against TgDHFR protein. Several studies have shown the remarkable ability of halofuginone (HF) to inhibit the activity of the prolyl-tRNA synthetase enzyme of Plasmodium falciparum and T. gondii34. This compound is a derivative of febrifugine, a natural quinazolinone alkaloid obtained from Dichroa febrifuga herb18. Considering the ability of this compound to inhibit the parasite, we decided to consider the Halofuginone-PRS complex as a control to choose the best alternative drugs. Cromolyn, Cefexim, and Lactulose drugs showed the strongest interactions with the PRS protein. The Toxoplasma life cycle is regulated by a family of CDPKs with no direct homologues in the human host35. Therefore, this protein can be a suitable target for drug design. C3-substituted pyrazolopyrimidine compounds are potent and selective inhibitors of CDPK124. For this reason, we chose TgCDPK1 in a complex with a bumped kinase inhibitor, RM-1–132, as a control for drug selection against T. gondii. Finally, Pentaprazole, Betamethasone, and Bromocriptine drugs had the highest potential for interacting and inhibiting of the CDPK1 protein. Our computational studies showed that drugs mentioned above showed strong inhibitory properties against T. gondii DHFR, PRS, and CDPK1 proteins.T. gondii using the computational screening of FDA-approved drugs. We performed a virtual screening of available FDA-approved drugs, and analysis via MD simulation, and MMPBSA. Finally, Ezetimibe, Raloxifene, Sulfasalazine, Triamteren and Zafirlukast drugs against TgDHFR protein, Cromolyn, cefixime and lactulose drugs against TgPRS protein and Pentaprazole, Betamethasone and Bromocriptine showed the best results against TgDHFR protein. These drugs may be tested for their beneficial role in toxoplasmosis infection, primarily in vitro, in vivo, and clinical trials.In this study, we evaluated potential TgDHFR, TgPRS, and TgCDPK1 protein inhibitors against T. gondii were retrieved from protein data bank (www.rcsb.org). Proteins were prepared for molecular docking. The ligands, ions, and all the water molecules were removed from the PDB files. Charges and missing hydrogen atoms were added to the DHFR, PRS, and CDPK1 proteins in the AutoDock Tools environment and were saved in pdbqt format to be used in the following steps.The structure of proteins of Dihydrofolate Reductase (DHFR) with PDB ID: 6AOH, Prolyl-tRNA Synthetase (PRS) with PDB ID: 5XIQ, and Calcium-Dependent Protein Kinase 1 (CDPK1) with PDB ID: 3SX9 from 2100 FDA-approved drugs were extracted from the PubChem database. Inappropriate, organic polymers and inorganic compounds were removed manually. Then the structures of the ligands were prepared by AutoDock Tools 4.2. The SDF format was converted to PDB format using OpenBabel (version 2.4.1). Nonpolar hydrogen bonds were integrated, Gasteiger-Marsili charges were assigned, atoms were set with AutoDock atom types, and rotatable bonds were assigned and saved in pdbqt format using AutoDock Tools 4.2.36. We also used the study that investigated the crystal structure of these proteins and their active site to characterize the grid box more precisely38. Summing up these results was used to locate the binding pockets for virtual screening. After marking the critical amino acids, grid boxes of 34 × 29 × 25 Å  for TgCDPK1 protein, 32 × 94 × 72 Å  for TgDHFR protein and 3.5 × 1.4 × 17 Å  for TgPRS protein with 1-Å grid spacing were selected by using AutoDock Tools 4.2.The binding site of the TgDHFR, TgPRS, and TgCDPK1 proteins interact with the TRC-2533, halofuginone, and RM-1–132 ligands , respectively was analyzed by LigPlot software39. The Smina function is based on a similar search algorithm. It represents the implementing of a different set of scoring functions that can be applied to both the ligand binding and ranking processes39. The docked complexes were visualized and analyzed using PyMol and Ligplot + software40.For structure-based virtual screening and rigid docking of selected drugs, we decided to utilize Smina Autodock software41 generated the TgDHFR, TgPRS, and TgCDPK1 proteins pharmacophore model based on the TgDHFR complexed with TRC-2533, TgPRS in complex with halofuginone, and TgCDPK1 in complex with a bumped kinase inhibitor, RM-1–132. These models were applied for the virtual screening of small-molecule compounds that block the binding site of T. gondii DHFR, PRS, and CDPK1 proteins. Pharmit is a web server that searches small molecules based on their structural and chemical similarity to another small molecule with the aim of identifying molecules that bind to a target molecule41.The virtual screening was performed in FDA-approved libraries containing over FDA-approved 2100 drugs.The Pharmit softwareT. gondii DHFR, PRS, and CDPK1 to identify the molecules that could have the most promising results in in vitro calculations. Among the drugs with the strongest interaction (Lowest binding energy) with T. gondii DHFR, PRS, and CDPK1 proteins, we chose drugs with lower side effects and cost-effectiveness.These molecules were screened by molecular docking calculations to evaluate their binding affinity to 42 and OPLS-AA force field43. Docked complexes were selected and placed in a triclinic box with a distance of 1 nm from all edges44. The topology parameters for the protein structure were prepared by the GROMACS program. Topology preparation and configuration parameters of each ligand were used using the PRODRG server. After that, drug–protein complexes were immersed in a simulation box filled with SPC (Simple Point Charge) water molecules45. To neutralize the simulation box in terms of electrical charge, an appropriate number of ions were added to the environment of the box. Finally, Na and Cl ions were used instead of solvent molecules in the simulation box, and the medium was neutralized. Three-dimensional periodic boundary conditions were applied to the system. For all MD simulations, the energy minimization process was comprised of two parts: firstly, systems were balanced at 300 K for 100 ps using NVT  and secondly, a 1000 ps NPT  equilibration of the system were performed using Parrinello–Rahman barostat to obtain constant temperature and pressure in 300 K and 1.0 bar. The long-range electrostatics was treated with Particle Mesh Ewald (PME) algorithm by 10 Å cutoff distance. Van der Waals (VDW) interactions were computed with a 1 nm cutoff. The Linear constraint (LINCS) algorithm was used to constrain the length of covalent bonds. Finally, for all MD simulations, the first 10 ns was taken as equilibrium time, leading to 40 ns with 2 fs time steps. After the necessary equilibrations, 100 ns production runs were performed for the docked complexes. To evaluate the stability of the protein–ligand complexes, RMSD, RMSF, RG, and SASA were calculated46.The protein–ligand complexes with the strongest interactions among docked complexes were selected for further evaluation by MD simulations by the GROMACS 2018 package47 via the g_mmpbsa package48. In this method, the binding-free energy is the result of the free energy of the complex minus the sum of the free energies of the ligand and the protein, as follows:Analysis of the docked complexes binding-free energies were performed by applying the single-trajectory MMPBSA method


Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields. Tm), which is the temperature at which 50% of the protein loses its native structure and activity or alternative defined as the area under the melting curve. In the past, measuring the stability of proteins required extensive work and resulted in limited data. However, with the advancement of mass spectrometry-based thermal proteome profiling (TPP), it is now possible to simultaneously monitor the thermal stability of thousands of expressed proteins in a variety of settings such as in vitro, in situ, or in vivo. TPP is based on the principle that when proteins are exposed to heat, they denature and become insoluble. The proteins that remain in the soluble fraction at different temperatures are quantified by protein mass spectrometry and combined across the temperatures to generate individual melting profiles [Protein stability refers to the ability of a protein to maintain its structural and functional integrity under various environmental conditions ,2,3. Whiprofiles ,8.Tm. For this purpose, several prediction methods have been developed that utilize a variety of features describing proteins based on their amino acid composition, physicochemical and surface properties, statistical and sequence potentials, and other protein characteristics.Making use of a mass spectrometry-based proteomic approach, an atlas of the thermal stability could be compiled that comprises 48,000 proteins across different species ranging from archaea to humans and covering melting temperatures of 30–90 °C ,8,9. As Tm values. Although classification-based predictors have demonstrated outstanding performance, they simplify the prediction task by reducing the number of output classes to a discrete set, whereas regression-based analysis captures the continuous nature of Tm values. However, defining the task as a regression problem also presents additional challenges, such as non-uniformity of recorded datasets and gaps in the modelled domain.In the past, building stability prediction models faced the challenge of limited data availability ,14,15,16Tm predictor. Our model incorporates experimental conditions used in the TPP experiment, the protein amino acid sequence, and the organism growth temperature. By using an appropriate sampling strategy, we trained the model for large-scale prediction and achieved significantly better results than current approaches such as ProTstab2 [Tm value prediction so far. Further, we can show that the resulting augmented TPP dataset captures structural and biological properties and allows us to dissect the structural features that impact protein stability.To address these challenges, we present a novel machine learning architecture, DeepSTABp, for the reliable regression-based prediction of cellular thermal protein stability. DeepSTABp is based on a transformer-based protein language model for amino acid sequence embedding and state-of-the-art feature extraction. It combines different popular deep learning techniques into a standalone end-to-end protein roTstab2 ,16, whicTms) [Assessment of the thermal stability of proteins was long hindered by limited available experimental methodologies. In recent years, the rise of mass spectrometry-based thermal proteome profiling (TPP) has allowed for the creation of proteome-wide snapshots of protein melting points (Tms) . ExtensiTms) . HoweverTm predictors, we decided to incorporate protein primary structure information into the model [The following three design decisions were directly informed by the analysis of the dataset: (i) In similar fashion to previously published he model ,16. Howehe model  , the Sample Pearson correlation coefficient (PCC), the mean average error (MAE), the mean squared error (MSE), and the root mean squared error (RMSE). When evaluating prediction results for the training dataset, all metrics indicate that model performance is indeed impacted by the choice of the training dataset, with the best result achieved consistently when using the EUS upon dataset assembly.TO assess model performance, we computed 5 common metrics for the evaluation of regression models: The coefficient of determination  and E. coli (82 protein complexes) versus a randomly sampled sets of proteins of respective organisms [Although regression evaluation metrics are a valuable means of measuring the performance of a model’s predictions, it can be challenging to determine the extent of biological information conveyed by the achieved level of prediction quality. In order to determine whether patterns observed in the analysis of TPP data can be replicated with the attained level of prediction accuracy, we calculated the variation in rganisms ,24  to high (blue) content of alpha helixes are found across the whole Tm range of the human proteome, other properties show more inhomogeneous shapes. In the case of “PiHelix” and “Accessible Protein Surface”, we see an antiparallel pattern. While proteins with highest amounts of pi-helices are more frequently found at higher melting points, the opposite is true for the latter. Both observations fit well with previous reports, as pi-helices and protein packing density were indeed linked to an improvement in thermal stability [The analysis revealed that protein-wise aggregates of protein secondary structure elements “AlphaHelix”, “BetaSheet”, “PiHelix”, “Turn”, “NoStructure” and “Accessible Protein Surface” show different patterns when related to predicted tability ,26,27,28Tm predictions by DeepSTABp and protein structure prediction-based inference of protein features allows for the identification of patterns which were previously linked to protein stability by experimental studies.In conclusion, the results indicate that the combination of in silico methods, namely the Tms for research purposes does not necessitate any installation procedures or substantial computational resources. Rather, users may acquire protein-specific predictions through a user-friendly web interface accessible at https://csb-deepstabp.bio.rptu.de (accessed on 4 April 2023) (https://github.com/CSBiology/deepStabP (accessed on 12 April 2023)) and provided as docker images, which renders the tool readily deployable within on-premise solutions. Alternatively, DeepSTABp can also be run on a desktop pc. The average execution time for a protein sequence of 1000 amino acids is on average 17 s on an AMD RyzenTm 9 6900HS or half a second using an NVIDIA GeForce RTX 3070 Ti.The utilization of DeepSTABp to predict protein il 2023) . The proProtein stability plays a crucial role in a variety of biological processes and biotechnological applications. The thermodynamic stability of a protein determines its folding, assembly and function, and therefore, changes in stability can have significant effects on protein properties and are frequently observed to be disease related. Hence, studying protein stability is an essential aspect of protein biochemistry and structural biology. Due to the sparsity of experimental data, computational tools are necessary to predict protein stability given in the form of melting temperatures.Tm predictor. In contrast to other predictors like SCooP, DeepTP, and BertThermo essentially classifying thermostable and non-thermostable proteins, Prostab2 and DeepSTABp both employ a regression model to predict continuous Tm values [The deep learning-based predictor DeepSTABp presented in this study employs a protein language model to embed amino acid sequences and advanced feature-extraction techniques. It integrates various deep learning methods into a self-contained protein m values ,16,17,18m values . Furtherm values ,31,32,33Our prediction has enriched the TPP dataset, enabling us to unravel the structural and biological determinants of protein stability. Of particular interest is the observation that proteins exhibiting low surface accessibility tend to be found at higher temperatures, which fit with previous reports of protein packing density positively influencing thermal stability ,26,27.The high-quality predictions of DeepSTABp have enabled us to investigate the biologically relevant formation of protein complexes, replicating the known evolutionary phenomenon whereby proteins within complexes exhibit similar Tm values. This highlights the potential for future models to incorporate information about protein complexes in order to enhance prediction accuracy. Going forward, we plan to incorporate additional experimental data to refine our model. Although our protein Tm prediction performance has been excellent overall, we acknowledge the model’s limitation in accurately predicting the effect of single point mutations on global protein stability. This is a common issue faced in protein property prediction that needs to be addressed and analyzed in the future . Howeverhttps://csb-deepstabp.bio.rptu.de (accessed on 4 April 2023)).In summary, our DeepSTABp algorithm is characterized by its high speed and reliability, making it well-suited for the analysis of proteins from diverse organisms and large-scale protein datasets. Our testing encompassed proteomes from a broad range of species, spanning from archaea to humans, and encompassing melting temperatures available in the meltome atlas. To support the in silico exploration of protein thermostability, we have made the DeepSTABp model available to other researchers through a user-friendly web interface (Tms), we reanalyzed obtained melting curves and determined individual protein melting points by fitting the following non-linear model:a being the asymptote, m being the slope, and Tmid denoting the mid-temperature of the fitted curve [Tm was obtained by finding the temperature where the fitted function reaches a value of 0.5. In order to retrieve only reliable, high quality Tms, only model estimates with an R2 score > 0.9 and temperature variance across biological replicates < 2 degrees were retained in the final data set.Datasets used for model training and evaluation in this study were derived from high-throughput mass spectrometry-based thermo-proteome profiling (TPP) assays. To achieve an extensive and homogenous collection of experimentally determined protein melting temperatures  of protein me atlas , which n studies ,36. The  Uniprot . Prior tTm of the respective organism. Each Tm in the original dataset is then replaced by a random draw out of the simulated distribution. The EUS strategy samples a total of 10,000 training items for each organism. All datasets are made publicly available in accordance to the FAIR principles and provided within the Annotated Research Context (ARC) available at: https://git.nfdi4plants.org/f_jung/deepstabp (accessed on 12 April 2023).We created 3 distinct training datasets by tuning the “naïve” dataset through 2 different sampling strategies, which we will refer to as “uniform sampling” (US) and “extended uniform sampling” (EUS). The US sampling strategy is carried out independently for each organism in the training data set. It uses a simulated continuous uniform distribution limited to the minimum and maximum https://github.com/agemagician/ProtTrans (last accessed on 5 March 2023)). This model has been shown to be an efficient and competitive feature extractor, which allowed for the construction of state-of-the-art predictors achieving best-in-class performance in tasks like protein localization or membrane prediction [To obtain a feature space spanned by numerical variables, protein sequences are often preprocessed by feature extraction routines that generate numerical embeddings of amino acid sequences. For the development of our algorithm, we chose to deploy the transformer-based protein language model ProtT5-XL-UniRef50 released as part of the ProtTrans project  available at: https://git.nfdi4plants.org/f_jung/deepstabp (accessed on 12 April 2023).In this study, we treated lgorithm  combinesTm values of certain proteins [The model architecture is split into four blocks. The first block was included to incorporate whether TPP experiments were carried out following the heating of intact cells or cell lysate, respectively, as this factor has been previously shown to affect measured proteins ,23. It cproteins .Tm  mutes the influence of single neurons with respect to the computation of gradients and thus parameter tuning during the training phase [These networks are accompanied by a third block, an independent neural network that also consists of a sequence of 2 fully connected layers with 20 and 10 neurons, respectively. This block uses frequently reported growth temperatures of the organisms of interest as an input which allows the model to learn how factors that are independent of protein sequence influence a proteins ng phase . The optng phase  algorithTms (y) and predicted Tms (ŷ). In summary, we computed five different metrics:To validate the performances of models during training and testing and to allow for a fair comparison to alternative approaches, different commonly used evaluation metrics were computed. Each metric measures the discrepancy between vectors of N experimentally determined The mean average error (MAE):The mean squared error (MSE):The root mean squared error (RMSE):Sample Pearson correlation coefficient (PCC):2):And the coefficient of determination  as follows:Tms, N being the number of proteins present in the group and Tm. As a control, we took a total of 10 random samples per protein in each protein complex group and computed the cv thereof.Since it was previously reported that proteins present in complexes possess similar thermal proximity coaggregation profiles, we aimed to analyze whether this is observation is reflected by eepSTABp . To annox Portal . For eacHomo sapiens were retrieved from the AlphaFold Protein Structure Database [Tms for Homo sapiens, we chose to first sort each protein into a Tm bin with the bin size determined using Silverman’s rule for Gaussian kernel density estimation applied to the whole dataset [Tm bin, we counted the number of proteins in the bin belonging to a quartile, yielding 4 Tm distributions per protein feature.Protein structure predictions for Database ,56. The Database ,58. Foll dataset . SubsequModel training and evaluation was implemented in the Python programming language (v.3.10.8) using the PyTorch (v.1.13.0), PyTorch Lightning (v.1.7.7), pandas (v.1.5.1), NumPy (v.1.23.4), and Transformers (v.4.24.0) library ,62,63,64