Acidimicrobiaceae TMED77 (T7RdhA) with potential for catalyzing the degradation of per- and polyfluoroalkyl substances (PFASs). AF2 models and experiments identified T7RdhA as a corrinoid iron-sulfur protein (CoFeSP) which uses a norpseudo-cobalamin (BVQ) cofactor and two Fe4S4 iron-sulfur clusters for catalysis. Docking and molecular dynamics simulations suggest that T7RdhA uses perfluorooctanoic acetate (PFOA) as a substrate, supporting the reported defluorination activity of its homolog, A6RdhA. We showed that AF2 provides processual (dynamic) predictions for the binding pockets of ligands (cofactors and/or substrates). Because the pLDDT scores provided by AF2 reflect the protein native states in complex with ligands as the evolutionary constraints, the Evoformer network of AF2 predicts protein structures and residue flexibility in complex with the ligands, i.e., in their native states. Therefore, an apo-protein predicted by AF2 is actually a holo-protein awaiting ligands.Despite the success of AlphaFold2 (AF2), it is unclear how AF2 models accommodate for ligand binding. Here, we start with a protein sequence from  This breakthrough, together with the developments of other tools including RoseTTAFold2, allow us to understand the protein structure–function relationships with atomic precision. The performance of AF2, however, was found to produce contradictory results in some assessments. For instance, it is the subject of debate whether AF2 fails to predict the impact of point mutations in protein structure3 and stability4; whereas other studies indicated that the structures and phenotypic effects of the point mutations can be correctly predicted5 or assisted6 by AF2. With these controversies in mind, further modifications to the AF2-predicted structures are required to appropriately understand the protein functions7, including the addition of ligands (cofactors and/or substrates). Moreover, as proteins are not static and generally perform functions in the cell but not in crystals, it is important to examine if AF2 can capture protein dynamics in aqueous environments8.AlphaFold29, the “key-lock” model10 was proposed to describe how proteins perform functions via ligand binding. In this model the protein is described as a lock awaiting the ligand as a key to unleash its function. Unfitted ligands —including water molecules—would fail to unlock the protein. In later studies, especially with the help of protein and protein–ligand complex structures that were becoming increasingly abundant, a refined “induced-fit” model12 was proposed which accommodates the conformational changes of the protein upon ligand-binding. To illustrate the long-range  effect of ligand binding, the conformation selection model was proposed14, in which the binding conformations pre-exist in the protein such that the ligand binding could spontaneously occur. Both sequences and structures  of the binding pockets in proteins are thought to evolve to facilitate association with different ligands15. Although only a small number of representative pockets in proteins have been estimated, ligand specificity of proteins may emerge in evolution without functional constraints16. In this regard most of the previously or currently recognized hard-to-degrade chemicals, including polymers17 and per- and polyfluoroalkyl substances (PFAS)18, could serve as the “keys” for certain lock proteins.Over 60 years before the first glimpse of a protein structure20 or can be accurately predicted. In the Protein Data Bank22, however, the majority of the structures derived from experiments  are complexes, including multimers (often with symmetry)23, as well as bound with cofactors24 and other ligands25. This information is as important as the protein structures themselves for informing the protein functions and mechanisms, ever since the first solved protein structure of myoglobin9. However, the initial structures that we obtain from AF21, AlphaFold-multimer26, or AF2Complex27 are apo-proteins, i.e., proteins without ligands. Given the important roles of ligands play in the protein functions, it is crucial to determine whether AF2 is suitable for predicting structure and function of such proteins. In another words, do the apo-proteins predicted by AF2 have the proper binding pockets (cavities) for the ligand binding? Here, we used a multiple-ligand protein to answer this question. As the PFAS substrate  can bind to the protein as a substrate, it has the potential for biodegrading the PFAS contaminants.With AF2 it is likely that every single sequenced protein has its high-resolution 3D structure available in the databaseAcidimicrobiaceae sp. A6 that, when cultured in the presence of either perfluorooctanoic acid (PFOA) or perfluorooctane sulfonate (PFOS), was able to defluorinate these chemicals with an observed release of fluoride ion, shorter-chain perfluorinated products, and acetate28. The key enzyme for defluorination of PFOA/PFOS was identified as a reductive dehalogenase subunit A (RdhA) in GenBank (id: MK358462.1)28. However, only partial sequence of this enzyme (A6RdhA hereafter) was available with a missing C-terminus of over 100 AA’s compared with known reductive dehalogenases including PceA29 and NpRdhA30. Sequence mining starting from the partial A6RdhA sequence revealed a full protein sequence from the bacteria TMED77 in a metagenomic assembly of the Mediterranean Sea microorganisms31, which shares 98% sequence identity with the known part of A6RdhA protein. This protein is referred to as the T7RdhA in present work. It is worth noting that the TMED77 bacterium belongs to the same Acidimicrobiaceae family as A. sp. A6.A recent work found a bacterium 29 which utilizes two Fe4S4 iron sulfur clusters and a norpseudo-cobalamin (BVQ) cofactor. We constructed AF2 models of T7RdhA, and for the highest-ranked model, both BVQ cofactor and Fe4S4 clusters can be put on the binding pockets precisely. Molecular dynamics (MD) simulations were performed on this model with no ligand (apo-form), partially bound by cofactors (either BVQ or Fe4S4), or with both cofactors, and with both cofactors and a substrate (PFOA). The results indicate that the AF2 is able to predict the binding pockets for both cofactors and substrates in the protein models, with regard to the binding pockets dynamics32. The model used in the MD simulation was constructed using AF2 V2.0.1 (July 2021 version). A newer version of AF2 V2.2.2 (downloaded in July 2022) was employed to construct additional 90 models and compared with the MD model. High similarity of the new models with the MD model illustrates the reproducibility of AF2. Interestingly, we show that the diversity of AF2 models resemble the MD results. We perform residue-interaction network (RIN) analyses using the MD trajectories of the model with both BVQ, Fe4S4, and PFOA. We identified the binding pockets for both cofactors and the substrate in T7RdhA, which will help to search and design proteins for PFAS biosequestration and degradation.In the present work, we showed that T7RdhA is a PceA-like protein33, T7RdhA and the T7RdhA-like proteins comprise highly conserved residues for the binding of a corrinoid cofactor and two Fe4S4 iron-sulfur clusters  in NpRdhA30 and B12-derivatives such as the norpseudo cobalamin (BVQ) in PceA29. NpRdhA uses the B12 cofactor and belongs to an aerobic bacterium Nitratireductor pacificus35. However, PceA that uses the BVQ cofactor is carried by the anaerobic bacterium Sulfurospirillum multivorans36. It is likely T7RdhA uses the BVQ cofactor not only the Acidimicrobiaceae bacterial family is anaerobic28, but also because T7RdhA belongs to the PceA branch , and Fig. From a sequence similarity network (SSN) constructed using the NCBI nr databasench Fig.  in the cers Fig. . Moreoveers Fig. . The netbalamin B2 in NpRd8. However, AF2 only provides the apo-forms of the proteins or protein-multimers, and the knowledge of cofactors and/or substrates related to the protein functions can only be acquired from experiments or literature. In the case of T7RdhA, since it is likely a CoFeSP which performs the functions utilizing the corrinoid (BVQ) cofactor and two Fe4S4 clusters, and presumably the PFOA substrate can be bound to the active site of the protein for catalysis, we asked if AF2 can predict the binding of these proposed ligands.We showed previously that the per-residue pLDDT  scores accompanying the predicted protein models by AF2 also anticipate the residue flexibilities for globular proteins, protein dimers and intrinsically disordered proteins4S4 clusters (Fe4S4-A and Fe4S4-B) and the PFOA substrate; (2) apo-T7RdhA with no ligand; (3) T7RdhA complexed with BVQ; (4) T7RdhA complexed with two Fe4S4 clusters; and (5) T7RdhA complexed with the BVQ cofactor and two Fe4S4 clusters. T7RdhA is a well-folded globular protein. The binding of cofactors or ligands does not significantly alter the conformation of T7RdhA. However, the residue flexibility vary significantly for all the five systems, as shown in Fig. 1. In addition to these five systems, we constructed 320 more AF2 structures and calculated the residue fluctuations among these structures for comparison  T7RdhA complexed with BVQ, two Febstrate;  apo-T7Rdbstrate;  apo-T7Rd8i is the AF2-score of the i-th residue calculated from the pLDDT-score of the Cα atom of the i-th residue8. For systems 1–5, 300 ns MD simulations was performed, and the RMSF profile was calculated using the last 100 ns trajectory. For all five models positive correlation between the AF2-scores and RMSFs were observed —except for an inconsistency at the C-terminus region for which AF2 anticipates it highly flexible but the RMSF from MD indicates it is instead relatively rigid. Nevertheless, for the important binding regions such as three β-sheets and the helices H12 to H16  are highly flexible, contradicting the AF2 predictions. In system 4, binding of the Fe4S4 lead to relatively small flexibility of the binding Cys residues, however, the loop region between H15 and H16 still shows significantly higher flexibility compared with the AF2 prediction. System 5 also shows better consistency between MD and AF2 prediction, slightly lower than system 1. We also measured the root-mean square deviation (RMSD) of all five systems. The mean RMSD of the last 10 ns are calculated as shown in Table For the well-folded globular proteins, the residue flexibility profiles measured by the root-mean square fluctuation (RMSF) from MD simulations were found to be highly consistent with the AF2-scores from the AF2 predictions, which is a reverse normalization of the pLDDT scores1. In this work, the T7RdhA model for the MD simulations was constructed using an old AF2 version . We used the newer AF2 version  to construct 320 T7RdhA models . These models show relatively low RMSD to the MD model  to the AF2-score profile. In this profile not only the BVQ- or Fe4S4-binding regions, but also the dynamic N-terminus is consistent with the AF2-scores. Therefore, from the protein sequence, AF2 not only provided dynamics information of all residues via the (pLDDT or AF2-scores)8, it seems that multiple AF2 runs can mimic the MD simulation, i.e., the ensemble of AF2 models were coverd by the structural ensemble generated by MD simulation; Fig. The original AF2 publication suggested that diversity of AF2 models  may yield new biological insights by predicting alternative configurations of the proteinsdij between the Cβ atoms (Cα for Gly) of residues i and j, and there is a contact between these two residues if dij is shorter than a criterion 38. A residue interaction network (RIN)39 can be constructed based on the contact map in which all residues are regarded as vertices and the contacts as edges. Here, we used a modified approach to define dij as the shortest distance between non-hydrogen atoms of two residues, and a cutoff of 3.5 Å is used to identify contacts. This approach would avoid potential false contact assignments, see Methods and Fig. 4S4-A and Fe4S4-B) and the substrate (PFOA) were treated as individual vertices, constructed from the final snapshot of the 300 ns MD is shown in Fig. The protein residue distance maps usually defined as the distance 4S4 clusters, as well as the PFOA substrate. The distributions of these residues are shown in Fig. 4S4 clusters and PFOA combined. The motifs involved in BVQ binding include short helical segments H2, H3, H4, and longer helices H12, H13, H16, and H17; two strands from β-sheet A and the β-hairpin C are involved in BVQ binding . In this simulation we used a reduced Fe4S4-A , an oxidized Fe4S4-B  and a reduced BVQ (with Co+1), see methods. H16 is involved in the interactions for BVQ, Fe4S4-B and PFOA. In particular, the aromatic residue W343 that is conserved in other T7RdhA-like proteins . Y213 from H12 has been considered to mediate the reductive dehalogenation in PceA (Y246 of S. multivorans PceA)29, and its interactions with BVQ (98%) and PFOA (40%) may be needed for potential defluorination. F47 from a small helix H4 also interacts with BVQ (100%) and PFOA (90%). We noticed that the residues interact with PFOA (> 50%) are either aromatic  or positively charged (R89), which may be a unique feature of the binding pocket for PFAS substrates.To capture the dynamics of the RIN, we constructed the RIN every 1 ns from the last 100 ns MD and monitored the residues that interact with the BVQ cofactor, both Feins Fig.  shows in40. Folding and functions of proteins may also be assisted by the substrate that they bind41. We asked how reliable are the AF2 models in depicting the structures and dynamics of proteins upon cofactor and/or ligand binding? This is a critical question to answer for protein systems with cofactor/substrate, especially for understanding the interactions among them, as well as for protein–protein interactions.AF2 opens an avenue in biology on which the functions and interactions mediated by proteins can be understood with the assistance of highly accurate atomic models. However, the structures predicted by AF2, either single-chain monomers or multi-chain oligomers, are in apo-forms, i.e., unbounded form. Even the necessary solvents are missing in the structures predicted by AF2. Cofactors play an important, sometimes essential role in protein folding and functions42, peptide binding43, and protein–protein interactions44. In this work, the functional T7RdhA structure incorporates the natural corrinoid norpseudo cobalamin (BVQ)45, together with two Fe4S4 iron-sulfur clusters (Fe4S4-A and Fe4S4-B), which is known as the “Nature’s modular structures”46. We showed that when cofactors (BVQ/ Fe4S4) and substrate (PFOA) are present in the correct pockets, the residue flexibility calculated from molecular dynamics simulations can best rationalize the AF2-scores by AlphaFold2, which is an inverse normalization of the pLDDT scores. In the complex model, the residue distance map also mirrors the predicted aligned error map by AlphaFold2. Our results indicate that the AF2 structures already have the pre-built pockets for the correct cofactors and ligands. We also showed that multiple AF2 structures (320 T7RdhA models in the present work) can also capture the protein dynamics. The diversity of protein structures, in our opinion, originates from protein dynamics and can be recaptured by AF2 in the structure modeling.Previous publications discussed the above question on ligand binding47 applies to all biomolecules, including proteins. We collected the structures of different systems  and iron-sulfur clusters (Fe4S4) by experiment. The binding mode of the cofactors and the PFOA ligand have been identified using a dynamic residue interaction network from the MD trajectories. We also showed that AF2 combined with MD simulation can help to identify proteins with targeted functions such as PFAS bioremediation.The persistence and accumulation of per- and polyfluoroalkyl substances, or PFASs, in the environment, and their adverse effects on human health have led to the current global concern50 multiple-sequence alignment, and were then used to construct an HMM profile using the program HMMer (v3.3.2)51. The first portion of the NCBI non-redundant database, nr00 , was queried using this HMM profile using the HMMer default threshold values. The resulting 1279 (including T7RdhA) sequences were submitted to the EFI (Enzyme Function Initiative) enzyme similarity tool for generation of the sequence similarity network (SSN) with evalues ≤ 10−5 and an alignment cutoff of 20 corresponding to an id% of ~ 3052. Network clustering and the T7RdhA clique identification was performed using the igraph package in R53. The multiple-sequence alignment by MAFFT was visualized using WebLogo (v3.6.0)54. TM-align55 was used for structure alignment and RMSD calculations. The calculated RMSD matrix was converted to phylogeny using the ape package in R56, and visualized by Mega-X57.A combined A6RdhA/T7RdhA Hidden Markov Model (HMM) was constructed from 529 non-redundant similar sequences identified via blastp from the NCBI and UniProt KB databases. Briefly, these sequences were identified to clade together  with A6RdhA/T7RdhA in an amino acid tree using MAFFT v7.453)1.The T7RdhA model used in the MD simulation and other T7RdhA-like proteins models 60 was employed for the protein and a modified TIP3P model61 for the solvent water molecules. The CHARMM-format force field parameters of norpseudo-B12 (BVQ)62 and Fe4S4 iron sulfur cluster63 under different redox states have been adopted. The force field parameters of the PFOA molecule were derived from the TEAM  force field in the Direct Force Field 64 software, and have been listed in the Appendix-The molecular dynamics simulations were performed using NAMD4S4 cofactors in the crystallographic structures of PceA  can be superimposed very well, with the eight Cys residues precisely bound the Fe4S4 iron atoms. We used TM-align55 to calculate the rotation-translation matrix between the PceA template (pdb 4UQU29) and the AF2 models, then applied this matrix to get the initial coordinates of both BVQ and Fe4S4 cofactors. All hydrogen atoms have been added using the HBuild function of CHARMM65. The covalent bond between Fe4S4 cofactors and their binding Cys residues were generated using the Patch function of CHARMM65. The Appendix-4S4 clusters in the model for MD. The whole system was put in a solvent box with H2O molecules added at least 15 Å to the edge of the protein system. The solvation and neutralization (using Na+ and/or Cl−) were carried out by the Solvate and Autoionization packages of VMD66. A reduced BVQ (Co(I)), oxidized Fe4S4-B 42−) and reduced Fe4S4-A 43−) were used in the MD simulations.The BVQ and FerigidBonds option was applied to fix the bond lengths involving hydrogen atoms and a timestep of 2 fs was used for the simulations. The van der Waals interaction cutoff switching was set as between 9 and 11 Å. For the long-range interactions, the particle mesh Ewald summation with a grid spacing of 1.35 Å was applied. 310 ns MD simulations were performed for all systems  ensemble was used in the MD simulation with the system pressure of 1 atm and temperature of 300 K maintained by the Langevin piston controls. The 39. A common approach, for example, is to measure the Cβ-Cβ distances (Cα for Gly), and if the measured distance between residues Ri and Rj is shorter than 8 Å, then there is a contact between Ri and Rj. This approach, however, we found may lead to incorrect assignment  Ri and Rj we define the distance dij as the shortest distance between all heavy atoms. The distance map under this approach  between Ri and Rj. We then construct a binary adjacency matrix (1 for interaction and 0 for non-interaction) based this definition. The network analysis was performed using the igraph53 package in R. The distance analysis was performed using the bio3d68 package in R. The BVQ cofactor, the Fe4S4-A and Fe4S4-B clusters, and the PFOA substrate was treated as a residue (vertex) in the RIN.The residue interaction network (RIN) or contact map of a protein was based on the distance map with a criterionent Fig. . We adop69 was used for ligand docking. Using the T7RdhA-BVQ- Fe4S4 system (system 5), after 10 ns MD equilibration, the PFOA ligand was docked into the protein complex (solvent and ions removed), and the top-score ligand was used to construct system 1. The force field parameters of the PFOA ligand can be found in the Appendix in SI.AutoDock Vina (V1.2.0)Supplementary Information.


Introduction: In the field of bioinformatics and computational biology, protein structure modelling and analysis is a crucial aspect. However, most existing tools require a high degree of technical expertise and lack a user-friendly interface. To address this problem, we developed a protein workstation called PROFASA.Methods: PROFASA is an innovative protein workstation that combines state-of-the-art protein structure visualisation techniques with cutting-edge tools and algorithms for protein analysis. Our goal is to provide users with a comprehensive platform for all protein sequence and structure analyses. PROFASA is designed with the idea of simplifying complex protein analysis workflows into one-click operations, while providing powerful customisation options to meet the needs of professional users.Results: PROFASA provides a one-stop solution that enables users to perform protein structure evaluation, parametric analysis and protein visualisation. Users can use I-TASSER or AlphaFold2 to construct protein models with one click, generate new protein sequences, models, and calculate protein parameters. In addition, PROFASA offers features such as real-time collaboration, note sharing, and shared projects, making it an ideal tool for researchers and teaching professionals.Discussion: PROFASA’s innovation lies in its user-friendly interface and one-stop solution. It not only lowers the barrier to entry for protein computation, analysis and visualisation tools, but also opens up new possibilities for protein research and education. We expect PROFASA to advance the study of protein design and engineering and open up new research areas. Commercially, synthetic proteins are poised to drive a major sector of synthetic biology . Proteinn design . During de novo protein design methods, as the protein structure database expands, protein structures with novel functions can be generated through AI. All these necessitate a lot of mathematical power, thus, as with structure prediction, deep learning might propel the study of protein design and engineering to new heights. There are many freely available protein tools on the market. For sequence alignment, BLAST has long been a widely used tool  algorithms for Multiple Sequence Alignments (MSA) generation; transformer-based approaches for protein structure prediction; DL-Based approaches for multi-domain protein structure prediction and so on . Howeversed tool . For strsed tool , and thesed tool . Apart fsed tool . Recentlsed tool . In edutsed tool . The aboPROFASA) is an in silico workstation for protein structure evaluation, parametric analysis, and protein visualisation. It provides i) a unified interface for rapid conserved domain analysis of input sequences and displays the results using a molecular visualisation UI ii) users can construct protein models with one click using the I-TASSER or the AlphaFold2, iii) it can fold numerous input sequences, create new protein sequences, models, and calculate protein parameters, iv) it uses tables and plots to display and compare all data, these results provide a guide to the expected protein performance prediction using Function2Form (F2F) plot (PROFASA : PROFASA2F) plot .Model generation timetable). Users only have to submit a sequence to get parameters like accessibility, instability, hydrophobicity, isoelectricity, size, and Ramachadran plot score (The number of parameters per tool). Drawing upon the precision of well-established bioinformatics tools, PROFASA provides accurate results across all its features. Its sequence alignment functionality uses the NCBI BLAST toolkit, which is known for its reliable sequence comparison capabilities. Structure prediction in PROFASA is handled through I-TASSERPROFASA key features).PROFASA has fast model generation and structural analysis time and is faster than most tools on the market, as shown in the (PROFASA architecture)Serving as a one-stop workstation, we aim to provide the users with a platform for all the protein sequence and structure based analysis. The architecture design specifies the goal, key functions, activity elements, and important processes, as well as the nature of their interconnection . Each moThe application architecture of PROFASA describes design and development patterns and techniques. When developing applications, a strong application architecture gives a roadmap and recommended practices to follow . ApplicaPROFASA uses TypeScript as its primary programming language because it operates in a web environment. TypeScript is a JavaScript superset that adds types, interfaces, and other useful features to JavaScript . ReactJSRamachandran Plot and F2F Plot). Two of the reasons for drawing manually rather than using an existing chart library, are the need for extensive flexibility and the need to keep the downloaded visuals consistent with what is displayed on the website. This is done with the Pixi.js library, which is the world’s fastest WebGL-2D rendering tool. In addition, PROFASA also has a document download feature for each protein sequence. Users can individually copy the sequence, download the PDB file, download the diagram, or download the complete PDF document. To display and download PDF files, the React PDF Renderer library is used.PROFASA uses Canvas to create a radar plot of F2F Result, allowing users to intuitively see and compare the data of each protein, as shown in  framework to build relationships between code data structures and database tables. Using Sequelize eliminates the need to write most of the SQL. It is very helpful for database migration and test environment setup. In version 2.0, the backend added the ability to take simple notes on the current model and the ability to share tasks between users. These features, although seemingly simple at the moment, are important changes in the long run when it comes to protein education and multi-person collaboration.PROFASA uses Node.js not just to establish servers, listen to requests, and read and write databases, but also to run a variety of command-line scripts to perform complicated features. Open-source third-party programs and Python scripts produced by the developers themselves are examples of this. Command-line programs are run in the main Node.js program using the Node.js standard library’s child process.exec  and child process.execSync  methods. The most of these command-line programs are C or C++ compiled programs, or C or C++ programs launched by Python or the Shell. This provides these programs a lot of power and performance. In other words, PROFASA’s back end acts as a glue that holds together C or C++ programs that perform well and efficiently, but are difficult to install, difficult to use, and expensive to learn. For example, AlphaFold requires not only sophisticated hardware but also programming skills; The calculation process of I-TASSER is usually measured in days and cannot run continuously on PC; RpsBlast’s command-line program is almost unreadable to non-experts. Despite the complexity of the back-end logic, users can access the results of all these fantastic programs through the PROFASA front-end interface, which is simple to use and comprehend. Among them, the RpsBlast program does conservative domain queries, which is one of PROFASA’s fundamental features. RpsBlast searched according to Conserved Domain Database (CDD) and obtained a set of protein profiles . Howeverisoelectric.org, and SAVES server, which are all well-known Protein APIs. PROFASA analyzes these API calls further and visualizes the intended outcomes, making it simple to learn about protein parameters and what they signify. As shown in (F2F plot parameters), it lists the parameters in the F2F plot, some of which may be accessed using the APIs mentioned above. Among them, as shown in MaxASA of amino acids).The PROFASA server also supports calls to RCSB search, Protparam, Database design).Because PROFASA focuses on front-end interaction and back-end command line calls and results processing, the database was the easiest one of the project. All PROFASA database tables have three common fields: ID (Private Key), CreatedAt, and UpdatedAt. For the details, as shown in (Users can try PROFASA using this link:https://profasa.ucc.ie/#/.PROFASA: PROFASA flowchart) is divided into four sections: Analysis, Superimpose, Structure Blast and the Fold. Each section takes a different input and provides outputs as discussed below.PROFASA , the user would upload a PDB file and PROFASA would compute all the parameters (as listed previously) and generate a PDF file.To compute and understand various structural, sequence based and functional parameters of a protein (RMSD and GDT TS).Superimposing two or more 3D structures is a powerful way to examine the differences in the structures. Biologists could use this for studying mutations, changes to functional hotspots, improving a novel fragment. PROFASA provides a 3D live image of the input PDB files, superimposed in the best configuration and calculates RMSD and GDT_TS to mathematically compare the difference between the structures (Structure blast) is one of the unique features that we have developed for PROFASA. Traditional sequence based BLAST finds matches for the full and parts of a user defined sequence. PROFASA extends this a step further by giving a 3D model for the hits. These hits could either be modeled using I-TASSER or Alpha Fold for unknown sequences. As a result the user would be able to visualize how a large complex protein can have domains and parts from various smaller protein fragments existing in nature.Structure blast . Hovering the mouse over the model reveals the amino acid at the present location as well as the sequence subscript. Additionally, the user could alter the color and style of the model area corresponding to a certain section of the sequence by selecting it by the sequence letter. Users also would be able to make notes on each structure and project. This level of interactivity makes PROFASA an excellent tool for teaching and bio edutainment.The most important feature of PROFASA is the interaction with individual models, which sets it more characteristic than other protein analysis tools, as shown in (PROFASA functional classification.Protein structure modeling and analysis is a crucial aspect in computational biology and bioinformatics, as it allows researchers to study the structure and function of proteins. Protein structures are complex, and determining their 3D structures experimentally can be time-consuming and expensive. Thus, computational methods are often used as a complement to experimental approaches. However, protein structure modeling and analysis are relatively difficult processes that frequently transition between many platforms or applications, posing a steep learning curve even for experienced computational biologists. Many of these tools and platforms require significant computational skills, as well as programming. This makes it challenging for biologists without a computational background to get started in the field. To address this problem, PROFASA, a one-stop protein structure modeling and analysis workstation, was created. The aim of PROFASA is to offer one platform for all protein sequence and structure analyses needs. It is worth noting that PROFASA has some important advantages over other existing tools, such as SAMSON Connect. For example, SAMSON ConnectPROFASA contributes significantly to the development of protein design as a one-stop workstation for modeling and analysis of protein structures. It combines into one platform many protein modeling and analysis tools such as, protein homology analysis, protein structure modeling, structure and function based parametric analysis, and protein structure superimposition. This enables researchers to quickly find potential ideas with a high chance of success without switching platforms. Another key advantage of the one-stop workstation is its ability to improve the accuracy and consistency of predictions by lowering the chance of practical mistakes and human error. PROFASA also enhances data management by enabling the integration and sharing of data obtained from multiple sources, facilitating the expansion of computational protein design. This can lead to the development of more sophisticated prediction techniques, and a large dataset can be accumulated to support the subsequent incorporation of machine learning algorithms. PROFASA has a user-friendly interface. This makes it easy to acquire and analyse protein structures for non-experts.PROFASA was created with its use cases in the education field in mind from the start. It features powerful interactive molecular structure modification and gamified interface for molecular structure viewing, including high-definition 3D model display. Such a gamified user interface offers an immersive and interactive learning experience in contrast to traditional passive teaching techniques like lectures and textbook reading, and it has been shown to be a successful tool for science teaching and learning . FurtherWhile PROFASA presents an innovative and efficient solution, one major challenge of PROFASA arises from the fact that PROFASA uses multiple external tools to perform its analyses, and the accuracy of the final results depends heavily on the accuracy of these external tools. Another challenge is that these analyses are computationally intensive processes, which can be both time-consuming and expensive to maintain. Additionally, given that the tool aims to generate large amounts of data, there is a need to ensure that the data is stored safely and securely, particularly if it is to be released openly. A further challenge is the need to continually improve the speed and accuracy of the tool, in order to keep pace with the rapidly evolving field of computational biology and bioinformatics.Ambitions and future outlook. PROFASA 2.0 would include an automated workflow system to enhance the user experience for researchers with less experience and to expand data accessibility. Automation of parameter optimization, prediction validation, mistake detection, and report production are some of the features included in this automated workflow system. In addition to making protein structure modeling and analysis much simpler, this will also enable timely self-checking, which will lower the data error rate and raise the system’s degree of confidence.PROFASA already is a powerful tool for researchers and educators. PROFASA 2.0 is currently being created with more advanced features and ambitions, see Furthermore, PROFASA 2.0 would be upgraded with high-performance computational resources, like server clusters and load balancing and specialised GPU resources. This would allow capabilities for multi-scale modeling at various resolution levels, including coarse-grained, all-atomic, and explicit solvents. Additionally, PROFASA 2.0 will incorporate more comprehensive datasets to enhance protein prediction speed and accuracy in the future. These databases may include experimental data, functional annotations, information on protein evolution, and known protein structures and properties. PROFASA 2.0 would also be accessible through virtual reality headsets that brings an immersive learning experience to the users. Finally, the PROFASA 2.0 interface would also be available in multiple languages to extend its potential user base and make it more accessible to researchers from other locations.


Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed usingTensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interfaceaccessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The presentmethod could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and forunderstanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent andtransient categories, and 5 different Supervised Machine Learning models were developed with  A host of biological and cellular activities, such as gene replication, transcription, translation, cell cycle regulation, signal transmission, and immune response, rely on protein-protein interactions. Protein-protein interactions (PPIs) are vital forunderstanding how proteins work together in the cell to accomplish biological tasks in a coordinated manner , 2. An eScikit-learn Standard Scaler utility. All descriptors were rescaled between 0 and 1.Dataset of protein complexes to study transient and permanent interactions were compiled from the literature , 20,21.Scikit-learn was used to construct the classification models and training the data to determine the best parameters for the training model using different algorithms such as k-Nearest Neighbour (k-NN) [Pandas v1.1.5, matplotlib v3.2.2, NumPy v1.19.5, SciPy v1.4.1, Scikit-learn v0.22.2 [seaborn v0.11.2 wereused to perform the machine learning. In all our models, the datasets were divided into training and test sets, in the ratio of 80:20. In k-NN, several distance metrics were evaluated in Scikit-learn, including k = 1 to 5 nearest neighbours,to predict the data. In Random Forest, the number of decision trees was set as 500. For Logistic Regression, different logistic regression classifiers have been employed by varying C value from 100 to 1000 and the best accuracy was achieved with C=500.The precision score, sensitivity or recall and F1 score, which is the weighted average of both the precision score and recall were calculated for each algorithm . Theseperformance measurements were calculated for each class that is transient and permanent and the geometric mean (G-mean) of sensitivity and specificity was also computed (r (k-NN) ,Logistir (k-NN) , Decisior (k-NN) , Random r (k-NN)  and Suppr (k-NN) .Pandas  v0.22.2 , and seacomputed . We perfcomputed .Tensor Flow and Keras to implement the deep learning. Deep learning models [We used g models  are madeadam as the optimizer for 43 input dimensions. To elucidate the relativefeature importance in transient and permanent categories, the feature contributions were also calculated. One of the most discriminating category of features in this classification procedure is interface areas, namely, Percent interface accessible area,Interface accessible area and Total interface area with feature importance score of 0.0437, 0.0436 and 0.036, respectively. The value of these parameters for transient PPI have been observed significantly lower as compared to permanent PPI. The average valueof Percent interface accessible area, Total interface area and Interface accessible area for transient PPI category have been observed to be 10.98%, 2594.4Å² and1291.2Å², respectively, as against 15.11%, 3819.1Å²and1911.5Å², respectively for in permanent PPI category.Based on 43 descriptors, several machine learning and deep learning methods were attempted to arrive at consensus results. The accuracy of the methods and other performance evaluation metrics were calculated and reported inThe second most important category of discriminating features is the one that describe the shape of interface such as Planarity, Eccentricity and Circularity with feature importance scores of 0.037, 0.034 and0.033, respectively. The Planarity describesthe rough or bent interface , 33 and Transient and permanent protein-protein interactions are significant in many biological processes. In the present work, we used a dataset, compiled from the literature and extracted physicochemical, geometrical and structural features from each ofthe 201 permanent and transient protein-protein complexes. Interface areas, shape of the interface and percent beta character are the three distinct categories of features, which prominently discriminate transient and permanent interactions. The method weproposed here could be useful in engineering permanent or transient PPIs, notably in the conversion of permanent docking interfaces to transient docking interfaces or vice versa using interface mutations . TheabiSupplementary Materials


B. stearothermophilus is a good model system for thermostability studies, since experimental data, such as crystal structure and thermal stability curves, are available. However, its unfolding mechanism at higher temperatures is yet unclear at a molecular level. Therefore, in this work, we researched the thermal stability of this protein using molecular dynamics simulations, subjecting it to five different temperatures during a time span of 1 μs. The analyses of the structural parameters and molecular interactions were compared with those of the mesophilic homologue HPr protein from B. subtilis. Each simulation was run in triplicate using identical conditions for both proteins. The results showed that the two proteins lose stability as the temperature increases, but the mesophilic structure is more affected. We found that the salt bridge network formed by the triad of Glu3-Lys62-Glu36 residues and the salt bridge made up of Asp79-Lys83 ion pair are key factors to keep stable the thermophilic protein, maintaining the hydrophobic core protected and the structure packed. In addition, these molecular interactions neutralize the negative surface charge, acting as “natural molecular staples”.The histidine-containing phosphocarrier (HPr) is a monomeric protein conserved in Gram-positive bacteria, which may be of mesophilic or thermophilic nature. In particular, the HPr protein from the thermophilic organism  Temperature is one important variable in the natural environment. The classification of the organisms based on temperature is distributed into four groups, psychrophiles, mesophiles, thermophiles, and hyperthermophiles ,2,3. AltBiomolecules, such as proteins, are part of these essential components and, according to their biochemical functions, they can be classified, in general, as structural, contractile, carriers, and enzymes. The functional diversity of proteins originated from the chemical variety of their building blocks (amino acids), the flexibility of the polypeptide chain, and the multiplicity of ways in which they reach folding ,5. It isMoreover, the ordered folding of the α-helices and/or β-sheets structures improves the intra- and intermolecular interactions, such as hydrogen bonds, hydrophobic contacts, and salt bridges . An incrIn addition to this aspect, there is an intrinsic and fundamental interest to understand at a molecular level the contribution of each non-covalent interaction to the thermal stability of such biomolecules. Different computational tools have been used for this purpose, in particular, molecular dynamics (MD) simulation methods have provided relevant information about these interactions, as has been reported in the literature ,14,15,16Bacillus stearothermophilus (BstHPr) has 70% sequence identity and 85% sequence similarity with the HPr protein from the mesophilic organism Bacillus subtilis (BsHPr), exhibiting very similar tertiary structures [BstHPr protein presents higher temperature resistance and 50% of its structural unfolding occurs at 88.9 °C, whereas the Tm of the BsHPr protein occurs at 74.4 °C [Homologous proteins can have a high percentage of identical and similar amino acids in their sequences. For example, the histidine-containing phosphocarrier protein (HPr) from the thermophilic organism ructures ,18,19. H 74.4 °C ,18. ThusBstHPr protein is analyzed and compared with its homologue BsHPr using molecular dynamics simulations. For this purpose, different structural parameters and molecular interactions were studied in both variants. It is worth mentioning that the HPr protein is an essential component of the bacterial phosphoenolpyruvate:sugar phosphotransferase system (PTS) that acts as a global regulator of signal transduction. This complex controls physiological processes related to prokaryote nutrition during bacterial growth [For these reasons, and because of the biomolecular importance of the HPr protein in the catabolism of different bacterial organisms, in this work the thermal stability of the l growth ,21.The results are divided into three subsections. First, we explore the structural behaviors such as secondary structure profiles, RMSD, Rg, and native contacts. Next, we analyze molecular interactions such as hydrogen bonds, hydrophobic contacts, and salt bridges. Finally, we investigate the electrostatic surface potential. The details are given as follows.BstHPr protein at 298 K and 1 μs simulation time. This profile is organized according to the protein’s primary sequence, starting with the N-terminal domain, which contains the secondary structures β1, α1, β2, and β3, and ending with the C-terminal domain, which includes the structures α2, β4, and α3.2-helix structure in the protein BstHPr is lost after 500 ns of simulation when the temperature increases up to 333 K. This structure is lost for both proteins at 362 K, but in the BsHPr protein, this occurs from the start of the simulation. In general, the α2-helix structure is very unstable and sensitive to temperature changes in both proteins, but the thermophilic protein presents greater thermal instability. To corroborate this effect, we have performed Ramachandran plots of this secondary structure at 298 and 333 K. Additionally, we carried out such analysis for the α3-helix structure at 298 K, which remains stable for both proteins, but the thermophilic protein displays greater stability than the mesophilic one. These details can be found in the BstHPr protein loses more α1-helix structure compared with its BsHPr counterpart; moreover, this transforms to a π-helix structure at the end of the simulation. Notice that, although the temperature modifies the spatial arrangement of the proteins, the anti-parallel structure of the β-strands is conserved at this temperature. Moreover, the α1-helix is lost at ≈200 ns, the α2-helix is not formed, and the α3-helix is lost at ≈500 ns for the two proteins at 400 K. A combination of α-helix structures is formed during the trajectories, i.e., remnants of α-helix structures remain and 310-helix arrangements are created. These α-helices can even be transformed into β structures, which occurs mainly in the BsHPr protein, e.g., β strands are generated at ≈450 ns in the α1 and α2 arrangements. At this temperature, the β structures are more affected in the mesophilic protein compared to its thermophilic homologue pair, since these are lost at certain time ranges. For instance, the β1 and β4 strands are lost between ≈800 ns and ≈940 ns, while the β2 and β3 strands are lost after ≈850 ns. In general, disordered structures, such as random coil, bend, and turn are formed when both proteins lose α-helices and β strands.Both proteins lose structural content along the simulations as the temperature increases. For example, at 362 K, the BstHPr protein maintains fragments of secondary structures more stable than its mesophilic counterpart at 450 K. For example, the adjoining β2 and β3 strands remain stable during almost the entire trajectory and the α3-helix is rarely present at ≈850 ns of simulation; afterward, they disappear. During this process, the β1 strand is the only stable structure in the BsHPr protein. It is important to consider that at this stress temperature, both proteins would lose their native secondary structures completely. However, the thermophilic protein maintains almost intact the β2 and β3 strands throughout the simulation.Finally, the 1, β2, and β3 strands of BstHPr protein and α3-helix of BsHPr protein are conserved at this time step at 450 K.Additionally, BstHPr protein in comparison with its mesophilic homologue one, when the temperature is elevated at 450 K. These facts can be consulted in the Similar behavior can be observed in the secondary structure profiles of simulations 2 and 3. In general, the β strands are more stable in BstHPr and BsHPr proteins at five temperatures of interest, while In BstHPr and BsHPr proteins are 0.102 ± 0.040 nm and 0.121 ± 0.024 nm at 298 K, respectively. BstHPr protein is slightly greater than that of the BsHPr protein. This is in contrast to the usual hypothesis that conformational rigidity determines thermal stability. However, Karshikoff et al. [f et al.  have shoBstHPr protein are lower than those found in BsHPr protein at 333, 362, and 400 K, which can be found in BstHPr and BsHPr structures at 362 K, respectively. This behavior indicates that the thermophilic protein’s structure is more stable than that of its mesophilic homologue pair, suggesting that temperature impacts cause the BsHPr structure to go through additional conformational states.However, these fluctuations are reverted as the temperature increases, i.e., the average and standard deviation RMSD values for the BstHPr and BsHPr protein, respectively. Although the RMSD average value of BstHPr protein is the lowest, it presents more fluctuations with respect to the BsHPr protein, because its standard deviation is greater (0.437 nm). Nevertheless, both structures lose stability, and thus unfold at this temperature.When both proteins reached the temperature of 450 K, their RMSD values increased by almost one order of magnitude with respect to their native state (298 K), yielding average values of 1.133 ± 0.437 and 1.225 ± 0.322 nm for BstHPr and BsHPr proteins at the five temperatures of study are shown in The average Rg values and their standard deviation values for the BstHPr and 1.185 ± 0.007 nm for BsHPr, and they maintain their native state during the entire simulation time . At the same time, the average value of native contacts in BsHPr protein is 0.832 ± 0.101, indicating that these contacts fall on average ≈10% in comparison with the native structure. The most important falls occur around 200 ns; after that the structure stabilizes, turning to a considerably decrease again at ≈700 ns  and 85% (BsHPr) on average of native contacts. As can be observed in BstHPr protein retains a higher fraction of these contacts.At the temperature of 450 K, the thermophilic protein has an average value of the fraction of native contacts of 0.199 ± 0.173 and its counterpart presents an average value of 0.142 ± 0.131. At this temperature, the proteins lose approximately 78%  vs. 2.2 (3.7%) at 333 K, 3.9 (6.2%) vs. 2.6 (4.4%) at 362 K, 9.3 (14.9%) vs. 8.7 (14.6%) at 400 K, and 20.8 (33.3%) vs. 17.7 (29.8%) at 450 K. It can be observed that in the temperature interval between 298 and 400 K both proteins lose almost the same percentage of HBpp (14.9% vs. 14.6%), but this percentage increases more for the BstHPr protein than for the BsHPr one in the interval from 298 to 450 K (33.3% vs. 29.8%). If the calculation is performed between 400 and 450 K, it is observed that thermophilic and mesophilic proteins lose 21.7 and 17.8 % of HBpp, respectively. The thermophilic protein loses more HBpp up to 450 K because it forms slightly more HBpp interactions compared to its mesophilic homologue pair at 298 K.First, we determined the average value and percentage of HBpp lost from the structures of the BstHPr and BsHPr proteins, respectively: 5.0 (2.7%) vs. 6.7 (3.2%) at 333 K, 11.9 (6.4%) vs. 15.1 (7.2%) at 362 K, 14.7 (7.9%) vs. 21.7 (10.4%) at 400 K, and 12.3 (6.6%) vs. 21.3 (10.2%) at 450 K. In this case, the BsHPr protein lightly loses more percentage of HBps than the BstHPr protein between 298 and 400 K. However, at 450 K these interactions slightly enhance, due to the structural arrangements between water molecules and proteins caused by the increased kinetic energy. Moreover, we can observe that in the range between 298 and 450 K the mesophilic variant loses more percentage of HBps compared whit the thermophilic protein (6.6% vs. 10.2%). This occurs because the BsHPr structure forms more HBps with respect to the BstHPr structure at 298 K.Secondly, these same measurements were made for the HBps loss, which are for the Therefore, both proteins lose HB in the temperature range from 298 to 450 K, but the thermophilic protein forms a greater amount of buried HB; in the meantime, the mesophilic protein forms a greater number of surface HB. These facts contribute to that the thermophilic protein is better packed and undergoes smaller global fluctuations see  and compBsHPr protein is 20.43 ± 0.68 nm2, while for the BstHPr protein is 18.37 ± 0.72 nm2. This is due to the fact that the BsHPr protein has a higher number of polar residues than the BstHPr protein, corresponding to 48.3 and 44.3% of the total residues, respectively.In its native state, the mesophilic protein presents higher SASAp values with respect to those observed in the thermophilic protein; the average value for the 2 and 22.06 ± 1.98 nm2 at 450 K, respectively. Therefore, from the native state to the denatured state, the BstHPr protein increases its area by 2.52 nm2 and the BsHPr protein increases this area by 1.63 nm2, which corresponds to percentage increases of 13.7 and 8.0%, respectively.The SASAp values increase for both proteins when the temperature rises from 298 to 450 K. The thermophilic and mesophilic proteins present average values of 20.89 ± 1.94 nmAlthough both proteins expose more of their polar residues to the solvent with increasing temperature, the thermophilic protein exposes a greater polar area than its mesophilic counterpart. This is possible because the mesophilic protein has almost a fully exposed the area of its polar residues to the solvent from 298 K, whereas the thermophilic protein has a greater buried polar area, which is shown to the solvent as the temperature increases.BstHPr protein is 30.89 ± 0.74 nm2, while for the BsHPr protein it is 28.84 ± 0.80 nm2. The average values of SASAnp increase for the two proteins as the temperatures increase too. In the temperature range from 298 to 400 K, the area of the BstHPr protein increases by 2.58 nm2 and the area of the BsHPr protein increases by 5.06 nm2, corresponding to an increase of 8.4 and 17.6% of each area, respectively. This means that the mesophilic protein undergoes greater exposure of its nonpolar residues to the solvent when it reaches 400 K. This finding is interesting to analyze since the hydrophobic core of the mesophilic protein is less protected, against energetic changes, than that of its thermophilic counterpart.By contrast, the thermophilic protein presents higher values of SASAnp than those observed in the mesophilic one in the native state; the average value for the BstHPr and BsHPr proteins, respectively, hence these areas change slightly for both proteins. However, between 362 and 400 K, the areas of the non-polar residues increase considerably by 7.4% for BstHPr and 14.9% for BsHPr. At 450 K, it is observed that the non-polar area increases significantly for the two proteins, whose average values are 42.93 ± 5.94 and 43.22 ± 5.13 nm2 for BstHPr and BsHPr, respectively. Therefore, from 400 to 450 K the non-polar area of both proteins increases almost the same percentage, 28.3% for BstHPr and 27.5% for BsHPr, indicating that the hydrophobic core of the proteins has been destabilized.In addition to this fact, it can be observed that in the temperature range between 298 and 362 K, the areas of the non-polar residues increase by 0.9 and 2.3% for BstHPr protein and 49.9% for the BsHPr protein. Thus, the thermophilic protein keeps a less unstable hydrophobic core than that of its mesophilic variant. This occurs because of three situations, (1) the BstHPr protein contains a greater number of non-polar residues , in this case, the structures of the BstHPr and BsHPr proteins have 49 (55.7%) and 45 (51.7%) residues of that nature, respectively; (2) as specified, the BstHPr protein has more buried HB; and/or (3) the BstHPr molecule presents a greater number of salt bridges that protect the hydrophobic core, which will be analyzed in the following In the temperature range between the native state and the unfolding state, i.e., from 298 to 450 K, the SASAnp increases by 39.0% for the BstHPr protein is more stable than that of the BsHPr one. Details of these calculations can be found in the Finally, we performed analyses of the ILV clusters for both proteins at 250, 500, 750, and 1000 ns using the ProteinTools tool . These aBsHPr and BstHPr proteins using the GetContacts tool at five different temperatures. As stated in the BsHPr and BstHPr proteins form 3 and 5 salt bridges in the native state (298 K), respectively.These tables show that the Glu84–Arg17 salt bridge has a frequency value of around 0.5 at 298, 333, and 362 K temperatures for both proteins. This means that this bridge is conserved and does not break, even if the temperature increases. This fact is probably related to the functional mechanism of the HPr protein, since the Arg17 and His15 residues have a strong relationship with the geometry of the active site. Indeed, the flexibility of the active site is important for the His15 residue to be phosphorylated ,26. ThenBsHPr protein at 298, 333, and 362 K. This salt bridge is favored due to the chemical nature of the Asp11 residue since the interaction between this residue with Lys or Arg is larger and the conformational entropy penalty is lower compared to salt bridges formed by the Glu residue [The Asp11–Lys57 salt bridge is also a conserved bridge in both proteins, but it forms more frequently in the  residue . The for3-helix and β1 strand structures , β2 (Glu36), and β4 (Lys62) strands, the triad (Glu3–Lys62–Glu36) causes the β-sheet, formed by the four antiparallel strands, to be more stable in the BstHPr protein with respect to the BsHPr protein when the temperature increases. As explained in Different investigations have shown that salt bridge networks are essential to stabilize protein structures ,31,32,33In addition to these facts, the analysis of the charged residues, using the PropKa server, showed that all of them are on the exterior of the two proteins, indicating that the salt bridges have a superficial nature. Consequently, each interaction between ionic pairs acts as a “natural molecular staple” that contributes to maintaining stable the hydrophobic core, corroborating that the triad of Glu3–Lys62–Glu36 residues is essential for the thermophilic protein to tolerate elevated temperatures.BsHPr protein there is a higher charge repulsion potential, thereby this structure undergoes greater structural destabilization, since the charge repulsion potentials cause that the proteins lose structural stability as the temperature rises [As all charged residues are placed on the surface of the proteins, the electrostatic surface potential was measured at 298 K. Thermophilic and mesophilic proteins showed a net charge of −1 and −4, respectively, indicating that on the surface of the re rises .3-helix of the mesophilic protein presents a higher negative ESP than that of the thermophilic protein ) of the meso- and thermo-bacteria Bacillus subtilis (PDB-id 2HPR) [Bacillus stearothermophilus (PDB-id 1Y4Y) [1α1β2β3α2β4α3 secondary structural configuration   and Baciid 1Y4Y) , respectguration  [17,37,3genion algorithm [First, both crystal structures were protonated using the PropKa server  at neutrlgorithm  was usedsteepest descent algorithm [−1·nm−1, and the potential energy was −2.68 × 105 kJ·mol−1. After that, it was necessary to equilibrate the temperature and pressure in the system. The equilibration of the temperature was completed using an extension of the Berendsen thermostat known as the velocity-rescaling method [−5 bar−1. Both NVT and NPT ensembles were equilibrated employing 100 ps.Long-range electrostatic interactions were calculated with the Particle Mesh Ewald (PME) method , using algorithm . This prg method . In thisg method  and one Hereafter, the LINCS algorithm  was usedBsHPr and BstHPr proteins, respectively [The temperatures used in this work were selected according to (a) 298 K corresponds to the temperature nearest to maximal stability (Ts) obtained from stability curves of the two proteins, (b) 348 and 362 K matches with the melting temperatures of ectively , and (c)ectively .define secondary structures of proteins (DSSP) [hbond algorithm of GROMACS [https://getcontacts.github.io/ (accessed on 16 March 2023)) and mindist function of GROMACS [freeSASA algorithm [In this work, we determined the following structural parameters in both proteins, (a) secondary structure profiles obtained with the algorithm s (DSSP) , (b) glos (DSSP) ,53, (c) s (DSSP) , and (d)s (DSSP) ,56. More GROMACS , (b) sal GROMACS , and (c)lgorithm ,58. Finalgorithm ,60. DetaBstHPr using molecular dynamics simulations at five different temperatures for 1 μs time period. To achieve this, we measured structural parameters and molecular interactions that were compared with those of the BsHPr mesophilic protein. The comprehensive analysis of these measurements sheds light on the stability of the BstHPr protein under the effect of temperature.In this work, we analyzed the thermal stability of the thermophilic protein 3-helix more unaffected, even at 450 K. Furthermore, it maintains the β2 and β3 strands stable during almost the entire simulation trajectory.We found that the native state structures (298 K) of the two proteins do not show significant differences. As the temperature increases, both proteins undergo changes in their secondary structures, but the mesophilic protein is the most affected. The thermophilic protein conserves the β-sheet and the αBstHPr and BsHPr proteins. The global fluctuations and the compact/expansion states are lower in the thermophilic protein, while the mesophilic protein loses more native contacts.The RMSD, Rg, and Q values allowed us to ascertain the structural differences between the 3 helix stables, function as “natural molecular staples” protecting the hydrophobic core and contributing to structural packing.Analysis of molecular interactions shows that the thermophilic protein, compared with its mesophilic counterpart, (1) forms more buried HB and less surface HB, but with increasing temperature, it loses a higher percentage of HBpp and a lower percentage of HBps; (2) has a lower polar area that is more exposed to solvent but exhibits higher nonpolar area that is less exposed to solvent if the temperature increases, indicating that its hydrophobic core is more stable; and (3) contains a greater number of salt bridges that help it resist temperature changes. In particular, the structural arrangements formed by the triad of Glu3–Lys62–Glu36 residues and the Asp79–Lys83 ion pair, that keep the β-sheet and αBstHPr and BsHPr proteins, respectively, causing higher charge repulsion potential in the structure of the latter protein. The thermophilic protein reduces its negative charge by the presence of the salt bridge network (Glu3–Lys62–Glu36) and the salt bridge (Asp79–Lys83), which are key interactions to maintain its thermal stability at elevated temperatures.Additionally, we calculated that 100% of charged residues are located on the surfaces of both proteins at 298 K. These residues produce net charges of −1 and −4 for the BstHPr protein, we have performed the mutation of the Lys62 residue by Ala62. However, such results will be reported and discussed in future work.Finally, in order to corroborate the important role played by the Glu3–Lys62–Glu36 triad in the thermal stability of