The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at  Structural characterization of protein interactome9Computational approaches to structure determination of individual proteins and protein–protein complexes have been rapidly progressing.There are several databases that report human protein–protein interactions  resource, which currently integrates knowledge-based tertiary structure prediction using Phyre2The GWYRE database provides mapping of human coding variations onto experimental and modeled protein structure and complexes, thus providing a valuable resource for the scientific community engaged in understanding how genetic variants affect phenotype.1.2,797 experimentally determined entries  proteins was downloaded from UniProt on August 25, 2021.2.907 “PDB + PDB” entries generated by docking two experimental structures (obtained from PDB). For these entries, data on 292,404 mutations for 646 unique proteins was downloaded from UniProt on October 11, 2021.3.586 “PDB + model” entries obtained by docking the PDB structure of one interactor and a 3D model of the other protein. For these entries, data on 226,624 mutations for 658 unique proteins was downloaded from UniProt on November 8, 2021.4.2,351 “model + model” entries obtained by docking two 3D models of the interacting proteins. For these entries, data on 366,181 mutations for 1352 unique proteins was downloaded from UniProt on September 1, 2021.The GWYRE database contains :In total, GWYRE provides structures for 6641 complexes onto which the location of 1,249,045 mutations is mapped. The overview of the GWYRE operational sequence is in All binary protein–protein interactions with both proteins from human (by taxonomy ID 9606) were imported from IntAct,The aim was to use our Phyre2 homology modeling serverPhyre2 was run in “normal mode” where a single PDB structure provides the template. As NMR structures provide an ensemble of structures, these were not selected as a template. Insertions and deletions were modeled by identifying PDB fragments that can be melded onto the fixed regions. Side chains were then added and the optimum packing of rotamers established as reported.•≥90% Confidence  from HHSearch.•The template used for Phyre2 had >20% sequence identity with the target sequence as defined by HHSearch.•No missing segments in the model of >30 consecutive residues either within the sequence or at the N- or C-termini.•α atoms of consecutive residues. A value of 3.8 Å x gap length in residue number + 1.2 Å was used.No unreasonably large distance between the C•i) radius of gyration < 0.8, i.e., the RMS distance of the center of mass of an object from its axis of rotation. It can be taken as a measure of the deviation from mmm symmetry, e.g., banana shaped as opposed to ellipsoidal; and (ii) the anisotropy of the principal component analysis (PCA) is <4.0; PCA is used to determine the ellipticity of a distribution. A spherical distribution has an anisotropy of unity, while prolate or oblate spheroids have larger values.To avoid elongated or severely flattened molecular envelopes, which may present difficulties in docking, a predicted structure had to meet the following two tests on its shape:  single particle cryo-electron microscopy. For most sequence queries, the top hit actually corresponded to the human template. The second situation arises when the Phyre2 template library only contains representative domains where no two entries have >70% sequence identity. Thus, there could be a structure of a human protein available in the PDB but not in the template library. Accordingly, where the Phyre2 template library did not contain an entry corresponding to a human protein, but an entry existed in the PDB, Phyre2 was run in the “one-to-one threading mode”, where the sequence of the protein from the UniProt entry is aligned against that from the individual PDB entry rather than against the entire fold library. The motivation for running Phyre2 when there is an available PDB structure for that sequence is that often the PDB entry can have missing atoms, and these would be modeled without introducing substantive conformational changes to the remainder of the protein where coordinates are available.Phyre2 produces a list of solutions, of which the best 20 were modeled, where the ranking is based on the E-value from HHSearch. The top hit that met the above criteria was selected except for two situations. The first situation is if there was a lower ranking Phyre2 hit derived from a human protein corresponding to the query UniProt sequence in the top 20 hits. This was selected provided the coordinates were obtained from either (i) a single-crystal diffraction  method or (A breakthrough in the modeling of tertiary structures occurred with the release of the second generation of the AlphaFold software.Dockground.α atoms is 0.86 Å and for 30–39% it is 2.79 Å).Most newly released PDB structures of protein–protein complexes have easily identifiable homologs among previously determined structures, which could have been used as templates for their modeling . Thus, template-based approaches to protein docking provide a viable solution to structural characterization of many protein–protein complexes. The template-based docking was performed on PDB structures  and modeled structures  of individual proteins by the full structure alignment protocol,In the future GWYRE development, we plan to extend pool of the docking models by including models generated by the partial structural alignment and free docking by GRAMMhttps://gwyre.org , as well as sequence identities for the individual models (if applicable), docking template and the overall docking score.The visualization page  utilizesThe GWYRE resource consists of PDB formatted files, each containing two docked proteins. For consistency, proteins are labeled ‘A’ and ‘B’ for the larger and the smaller protein  in the pair, respectively. The chain IDs may differ from those in the original PDB file. Residues in the GWYRE PDB-formatted files are renumbered to correspond to the numbering in the full canonical UniProt sequence. This ensures correct structural mapping of the variants. Sequences, features of the individual proteins and interaction details are stored in a PostgreSQL relational database, which is queried using SQL statements. The web page is written in PHP and JavaScript. Processing of the data before and after docking is performed by R scripts.https://www.gwyre.org, facilitates better understanding of principles of protein interaction and structure/function relationships.Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and PDB. The GWYRE project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and single amino acid variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by GRAMM. The predictions are incorporated in a comprehensive public resource for structural characterization of interactomes and assessment of phenotypic effects of genetic variation. The utility to download coordinates of both experimental and predicted binary complexes of interacting human proteins from GWYRE facilitates further analysis including computational assessment of the effect of missense variants using approaches such as FoldX,Sukhaswami Malladi: Software, Data curation, Validation, Visualization, Writing – original draft. Harold R. Powell: Software, Data curation, Validation, Writing – original draft. Alessia David: Methodology, Investigation, Data curation, Writing – original draft. Suhail A. Islam: Software, Data curation, Resources. Matthew M. Copeland: Software, Data curation, Visualization, Resources. Petras J. Kundrotas: Funding acquisition, Project administration, Supervision, Methodology, Writing – review & editing. Michael J.E. Sternberg: Funding acquisition, Conceptualization, Project administration, Supervision, Writing – review & editing. Ilya A. Vakser: Funding acquisition, Conceptualization, Project administration, Supervision, Writing – review & editing.


Protein oligomerization has two notable aspects: it is crucial for the performing cellular and molecular processes accurately, and it produces amyloid fibril precursors. Although a clear explanation for amyloidosis as a whole is lacking, most studies have emphasized the importance of protein misfolding followed by formation of cytotoxic oligomer structures, which are responsible for disorders as diverse as neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, and metabolic disorders, such as type 2 diabetes. Constant surveillance by oligomeric protein structures known as molecular chaperones enables cells to overcome the challenge of misfolded proteins and their harmful assemblies. These molecular chaperones encounter proteins in cells, and benefit cell survival as long as they perform correctly. Thus, this review highlights the roles of structural aspects of chaperone protein oligomers in determining cell fate—either succumbing to amyloid oligomers or survival—as well as experimental approaches used to investigate these entities.  •Constant surveillance of proteostasis network secures protein homeostasis.•Impaired proteostasis network results in accumulation of misfolded proteins and aggregate deposits leading to amyloidosis.•Numerous studies emphasize the significance of cytotoxic oligomeric assemblies as amyloid fibril precursors.•Molecular chaperones ensure effective protein folding via the formation of oligomeric protein assemblies.•Developed microscopic and spectroscopic methods opened new horizons to investigate protein oligomers structure and dynamics. Structure-function relationships confer various biological functions on single proteins with different assembly states. Amyloidogenic protein oligomers that emerge during protein aggregation, as well as the molecular chaperones that manage them, are the best fits for this model of protein assembly, in which oligomeric and monomeric states of the same protein have differing biological roles. Although many studies have examined protein aggregation, the entire process, the mechanisms underlying the formation of fibrils from monomers and the features that lead to cellular failure are not fully understood. Nevertheless, oligomers, the crucial precursors of cytotoxic amyloids, have attracted increasing interest in recent decades . In our 2in vivo protein aggregates form mostly amorphous assemblies (disordered aggregates), which are relatively tolerable for living systems, although protein aggregation can also result in the formation of amyloid fibrils (ordered aggregates) via the emergence of cytotoxic oligomeric intermediates, which affect cells' integrity and function, and thus play critical roles in diseases. These ultra-stable amyloid fibrils share a common core architecture consisting of a cross-β structure, in which β-strands align perpendicularly to the long fibril axis. Failure of a protein to adopt its native structure can result in a broad spectrum of disorders called protein misfolding diseases or amyloidosis, including neurodegenerative diseases, such as Parkinson's, Alzheimer's, Huntington's and prion diseases, and type 2 diabetes  form through the folding process to become biologically functional. Accordingly, folding of nascent polypeptide chains is conducted through a complex free-energy landscape involving numerous weak non-covalent intramolecular contacts, including hydrophobic forces, and hydrogen bonding, Coulombic, and van der Waals interactions  HY. In the diabetes .Fig. 1ScBoth natively folded proteins, such as insulin, transthyretin, β2-microglobulin, and lysozyme, and intrinsically disordered proteins, such as amyloid-β peptide (Aβ), α-synuclein (α-Syn), prion protein, microtubule-associated protein (tau), islet amyloid polypeptide (IAPP), and Huntingtin exon 1, are the best-known proteins that cause amyloidosis .Notably, functional oligomers as assemblies of native proteins are distinguished from amyloid oligomers by their more stable, compact, cooperative, and highly symmetrical structures, which occur mostly in homo-oligomeric forms with a constant number of subunits B. In conTraditionally, protein aggregation kinetics describes fibril formation with a sigmoidal rate profile via a nucleation phenomenon . In thisThe cytotoxicity of amyloidogenic species directly correlates with their size and the surface exposure of their hydrophobic patches, because when regions that are normally buried in the core of the correctly folded structure are instead exposed, they interact with cellular compartments. These molecular contacts increase membrane permeability and facilitate unlimited translocation of amyloidogenic species through membranes, and therefore are potentially harmful. Thus, amyloid oligomers  with more exposed hydrophobic surfaces are the main causes of cytotoxicity, rather than misfolded or higher order aggregated species. Therefore, amyloid oligomers can damage a broad spectrum of biological processes  Stefani.Fig. 2Me3de novo folding and/or refolding, assembly/disassembly of protein complexes, protein translocation, and disaggregation and degradation , with highly conserved sequences, to ensure efficient protein folding in all kingdoms of life via the formation of specific homo- or hetero-oligomers . Under nradation . In eachradation B. For inradation C.Fig. 3MSometimes the substrates released from this pathway are partially folded intermediates that must target the Hsp90 (molecular clamp) or the TRiC/CCT (folding cage) by Hsp40/Hsp70 complex to complete folding. Interaction of the Hsp40/Hsp70 complex with open state Hsp90 via the TPR domain (red linker in Extracellular chaperones (ECs) such as clusterin and α-crystallins are thought to prevent misfolded extracellular protein aggregation by stabilizing them (capping their exposed hydrophobic patches) until folding-assisted chaperones or proteolytic machinery are available to either refold or degrade the client proteins. Sometimes ECs prefer to associate these substrates into larger aggregates in order to prevent their further propagation and infecting other cells. Studies have also suggested that ECs indirectly induce the cytokine cascade, thus triggering inflammation by misfolded extracellular proteins F Wyatt .4Molecular chaperone assisted protein folding in balanced (no-stress) cellular conditions was discussed in detail above. Here, critical roles of molecular chaperones in maintaining or regaining the functional conformations of the proteins in unbalanced (Stressed)cellular conditions and various pathways are discussed below.Aggregation prevention. This pathway involves most Hsp family members, which buffer aggregation-prone intermediates  by refolding them into the native state. The sHsps, principally ATP-independent chaperones and an archetype for holdase, bind non-native species through hydrophobic interactions and cap exposed hydrophobic patches. Then by joining Hsp70 as a hub and other assisting co-chaperones  assisted disaggregation, and Hsp110 assisting disaggregation, which target a variety of aggregated substrates . Ligase CHIP (co-chaperone) complexation with Hsp70 (via their TPR domain), which binds client proteins, mediates ubiquitination and guides them to the cytosol and nucleus, where proteasomes are localized . Optical tweezers integrated with fluorescent particle tracking are powerful methods to describe how chaperones bind their substrates, ensure their accurate folding (folding mechanism) and reverse protein aggregation by disaggregating formed oligomers  are considered a crucial part of living systems, which perform diverse biological functions. Stress conditions may result in formation of aberrant oligomers (amyloidogenic oligomers), which threaten cellular function and integrity. Therefore, to better understand protein oligomer functions, structurally investigating these entities and developing efficient methods to monitor their conformational and dynamic features will be crucial.Payam Arghavani: Project administration, Conceptualization, Writing – review & editing, Visualization. Mitra Pirhaghi: Investigation, Visualization, Conceptualization. Faezeh Moosavi-Movahedi: Writing – original draft, Validation, Conceptualization. Fatemeh Mamashli: Investigation, Writing – original draft. Elnaz Hosseini: Investigation, Writing – original draft. Ali Akbar Moosavi-Movahedi: Supervision, Project administration, Conceptualization, Visualization.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN  and the FFNN  hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein–ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein–ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein–ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson’s R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein–ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform. Accurate methods of computing ligand affinity with a biological target are strongly desirable in drug discovery. Millions of compounds can be classified as ‘active’ and ‘inactive’ for a specific target using different virtual screening approaches. A ligand binds to the target protein and forms a protein–ligand complex that produces the effects such as inhibition and activation of a protein. Predicting the binding affinity and binding interaction strength between the ligand and the target protein is a key step in drug discovery. Determining the binding affinity helps identify the best ligand as the potential drug candidate. Computational approaches to predicting binding affinity instead of in vitro experiments significantly reduce drug discovery time and cost. The early phase of the small molecule drug discovery deals with the rational drug design for a protein target. Potential small molecules are selected as hits based on their binding affinity. Several computational methods have been developed to predict protein–ligand binding affinity ,4,5,6,7.RF. Deep learning models are popular due to their proven high performance in the field, such as visual data recognition [DEEP, developed by Jiménez et al. [The machine learning techniques use the experimentally derived protein–ligand data to learn and train their models to predict the binding affinity of a new protein–ligand complex; however, some challenges still remain, such as accurate treatment of protein–ligand interactions, protein flexibility, appropriate descriptors, and wide variations in the ligand affinity values ,6,7,8,9.ognition , speech ognition , and druognition . Severalognition  is a deeognition  developez et al. , uses thz et al. . developz et al.  by Öztürz et al. ,28,29,30In this study, we developed a new hybrid neural network (HNN) deep learning model comprising the ‘HNN-denovo’ and ‘HNN-affinity’ methods that use a separate learning framework to increase prediction accuracy. The HNN model combines the CNN and the FCNN machine architectures to model the protein structure, protein–ligand interactions, protein sequences, and ligand descriptors. The HNN-denovo method uses the protein pocket structure and ligand interactions as input features, whereas the HNN-affinity method uses protein sequences as input features. In both cases, we modeled the ligands by the SMILES string, which is used for the ligand descriptor calculation. The ligand SMILES and descriptor features are modeled separately by the CNN framework. Overall, the HNN method consistently outperforms the ligand binding affinity prediction deep learning methods published in the literature.In this study, we developed two hybrid neural network deep learning methods, called ‘HNN-denovo’ and ‘HNN-affinity’ for the de novo design and to predict binding affinity. We applied the HNN methods to rapidly screen a precompiled known library of protein–ligand complexes and predicted their binding affinity. The overall schema workflow of the HNN method is displayed in We used HNN learning than stacking learning for the following reasons. Stacking learning considers several models based on the different algorithms as the weak learner. Further, several models based on different algorithms are developed on the same training set in stacking, and the predictions are made for the same test set. Then the predictions are stacked and taken as the meta model’s input features to make a better final prediction. Whereas the HNN learning first makes models based on the CNN that considers ligand features and the FFNN that considers the protein feature descriptors. Then we concatenate the CNN and FFNN models but not their individual predictions to make the final prediction.d, Ki, and the combined Kd and Ki values from a general set and refined set obtained from the PDBBind database. To train the machine learning models, we computed a total of 653 protein- and ligand-specific molecular descriptors that were modeled by FFNN and the SMILES as ligand chemical features that are modeled by CNN. We also developed models based on RF, GB, and DT. We then predicted ligand binding affinity for the precompiled known library of protein–ligand complexes obtained from the PDBBind database. Since the HNN model suffers from higher variance in comparison to other machine learning methods, such as RF, we decided to make predictions multiple times and evaluated the performance based on the average of ten simulations. Though other machine learning methods, such as the RF method, has low variance with multiple simulations, to be consistent, we ran ten simulations for the RF, GB, and DT methods as well. The average of the RF, GB, and DT model predicted values were used for the consensus prediction. Next, we compared the HNN method-predicted protein–ligand affinity results with the RF, GB, and DT methods. We also compared the HNN-denovo and HNN-affinity performance metrics with the literature-reported deep learning methods such as DLScore, K-DEEP, and DeepAtom. The results are discussed in the following sections.We included a new SMILES feature representation method in the HNN framework by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN) ,33. We aRMSE), mean absolute error (MAE), and Pearson correlation coefficient (PCC). The MSE is the average of the squares of the errors, which are the differences between the predicted and actual values. The MAE is the average absolute difference between the predicted and the actual values. PCC measures the strength of the relationship between the two datasets of predicted and actual values.ith dependent variable, ith observed dependent variable, The performance of the models was evaluated as the average of 10 simulation results based on root mean squared error  is the combination of the Kgorithms , and the methods .d binding data: Case 2 data set.Models based on Kd and Ki binding inhibition data in this case as separate data sets, case 2 and case 3, respectively. The case 2 binding data set (see methods section) comprised of Kd binding affinity data compiled with 2464 protein–ligand complexes. The models were developed on the 2004 complexes with Kd values in the training set, and the test set contained 460 complexes for which binding affinity was predicted. The HNN-denovo model used 348 BINANA protein–ligand interactions descriptors, protein pocket structure, and the SMILES of the ligands as input features, whereas RF, GB, and GT models used BINANA descriptors as the input features. The PDB codes of the complexes in the training and test sets for case 2 are provided in d binding data is optimal or higher than the RF, GB, and DT  comprised the Ki binding affinity data compiled with 2386 protein–ligand complexes. The models were developed for the 1936 complexes with Ki values in the training set, and the binding affinity was predicted for the 450 complexes in the test set. The HNN-denovo model used 348 BINANA descriptors, protein pocket structure, and the SMILES of the ligands as input features, whereas RF, GB, and DT models used only BINANA descriptors as the input features. The PDB codes of the complexes in the training and test sets for case 3 are provided in i data, the HNN-denovo model performed optimally in predicting the ligand binding affinity, while the RMSE and MAE, among the models, are similar  were used as input for the CNN, and Ligand SMILES were used as input for the FFNN. We used the BINANA descriptors as the input features for the RF, GB, and DTBoost models, as they cannot handle SMILES input features. We used the case 1 binding data of the PDBbind v2019 refined set (see methods section), which is the combination of the KNext, we sought to evaluate the performance of our HNN methods, with respect to the literature-reported ligand affinity prediction deep learning methods ,14,15,16The ‘refined set’ and the ‘general set’ consisting of experimentally measured binding affinity data for biomolecular protein–ligand complexes reported in the Protein Data Bank (PDB) were downloaded from the PDBbind database .d or Ki inhibition binding constant values. From a total of 4850 protein–ligand complexes, 2464 complexes with Kd data and 2386 complexes with Ki data were processed to calculate the protein and ligand-centric descriptors of the complexes. The binding affinity Kd and Ki are converted to pKd and pKi (logarithm of the inverse of Kd and Ki) to predict-ligand affinity (pKd or pKi) using machine learning methods.Refined Set: The refined set from PDBbind v2019 consists of 4852 protein–ligand complexes annotated with the ligand affinity data. The ligand affinity data were provided as KCase 1.d and Ki binding affinity data with a total of 4850 protein–ligand complexes. Models were developed with a training set 3925 protein–ligand complexes containing both Kd, and Ki data, and 925 complexes were used as a Test set.The case 1 data set consists of a combined KCase 2.d data. Models were developed with a training set of 2004 protein–ligand complexes with Kd binding affinity data, and the remaining 460 complexes were used as a test set.The case 2 data set consists of 2464 protein–ligand complexes with KCase 3.i data. Models were developed with a training set of 1936 protein–ligand complexes with Ki binding affinity data, and 450 complexes were used as a test set.The case 3 data set consists of 2386 protein–ligand complexes with Kd and Ki data were processed and combined and the Kd and Ki data and obtained a total of 6081 protein–ligand complexes. Models were developed with 5031 complexes in the training set and 1050 complexes in the test set.General Set: The PDBbind v2019 general set consists of binding affinity data for 12,800 protein–ligand complexes. The KRefined-General Set: Refined-General Set contains a total of 10,931 complexes. The refined-general set was obtained by combining 4085 complexes from the refined set and 6081 complexes from the general set. We used 8931 complexes for the training set, and 2000 complexes were randomly selected for the test set.We used the protein pocket structure, and protein-ligand interactions modeled by BINANA descriptors as input features for the HNN-denovo method. Additionally, for the HNN-denovo method, protein sequences (described in the section below) were used as additional features. The protein and ligand files in .pdb and .mol2 format were first converted to .pdbqt format by prepare_receptor4.py and prepare_ligand4.py, respectively, MGLTools . The BINFor the deep learning method, SMILES were used as one of the features of the protein-ligand complexes. The SMILES of the ligands were calculated from their .mol2 files using Schrodinger’s structconvert utility. SMILES are in text format and were encoded as numbers to be used as input for the CNN. Unique indexes were created for 94 characters from ‘!’ to ‘~’ in the ASCII table to represent all possible SMILES characters in any format. The mapping of each character to a unique index was stored in python’s dictionary Dsmi = {‘!’:1, ‘”’:2, ‘#’:3, ‘$’:4, …, ‘C’:35, …, ‘~’:94} and made the vocabulary of possible characters in the SMILES. The SMILES of each ligand were encoded with unique numbers and either truncated or padded with 0s to the maximum allowed length L. The SMILES for the input compounds were converted to a 2D matrix of size K × L, where K was the number of input SMILES, and L = 325 was the allowed maximum length of the SMILES string used in the model.We also used protein pocket sequences as input features for the HNN-affinity method. The sequences of the amino acids were extracted from the files in the .pdb format provided by PDBbind for the binding pockets of the protein-ligand complexes. The pocket sequences, also in text format, were number-encoded to be used as input for the CNN. Each of the 20 alphabetical characters representing the 20 amino acids was mapped to a unique index by a dictionary, Dseq = {‘A’:1, ‘R’:2, …, ‘V’:20}. The sequence of each pocket was encoded with unique numbers and either truncated or padded with 0s to the maximum allowed length M. The sequences were converted to a 2D matrix of size K × M, where K was the number of input sequences, and M = 150 was the allowed maximum length of the sequence used in the model.The deep learning-based hybrid neural network model  was deveHyperparameter optimization was implemented using the tree-structured Parzen estimator (TPE) method within the hyperopt package of python to search for optimum parameters to improve the model’s performance . This apTo compare the HNN-affinity prediction results, we also developed regression models based on machine learning methods based on the random forest (RF), gradient boosting (GB), and decision tree with AdaBoost (DT). A consensus prediction was made based on the average prediction rate of the three methods, excluding the HNN.DK, IK, and IC50 as it is trained on thousands of known protein-ligand complexes with known DK, IK, and IC50 values.In this study, we developed, implemented, and validated the HNN-denovo and HNN-affinity for the ligand binding affinity prediction in the de novo design campaign. The HNN-denovo and HNN-affinity confer a higher prediction rate and most of the time successfully retrieve the cognate ligands for the proteins. The present versions of the HNN-denovo method reached the highest prediction accuracy, with a Pearson correlation coefficient (PCC) of 0.86, i.e., 86% of the time, it correctly predicts the native ligands based on the ~9000 protein-ligand complexes obtained from the PDB-Bind database. The HNN-denovo can generate small molecule hits specific to a protein binding site under the guidance of a drug-target binding affinity prediction structure model. The HNN-affinity can generate small molecule hits specific to a protein binding site under the guidance of protein binding site amino acid sequence patterns. The known limitation of the structure-based machine learning methods in predicting ligand binding affinity is that they require a protein-ligand experimental structural complex or an accurate docked ligand structural complex. The absence of such a protein-ligand structural complex or inaccurate structural complex will lead to an inaccurate ligand affinity prediction. Consequently, a machine learning prediction method that includes the protein sequence-based prediction framework is needed. Our HNN-affinity method can predict ligand affinity based on the protein amino acid sequence as input features in the absence of the protein structural information. Notably, HNN-affinity is particularly useful when the protein-ligand complex structure is unavailable; when dealing with undruggable targets; and when targeting intrinsically disordered proteins. Further, the HNN-affinity can predict different binding affinities such as In summary, we have demonstrated that the HNN method shows promising performance for diverse data sets and outperforms the other deep learning methods for balanced and unbalanced data sets. The promising results on various datasets demonstrated that our HNN method could be potentially implemented in combination with molecular docking-based virtual screening campaigns, particularly when screening billions of compounds. We are adapting the HNN method with the molecular docking-based large-scale virtual screening pipeline. The HNN method is highly scalable and uses Keras and Tensorflow to generate and train the neural networks. The HNN virtual screening simulations can be performed with high-performance GPUs on the cloud to screen billions of diverse compounds rapidly. We ran it in our powerful GPU Linux server, which is highly scalable to run on the cloud ML platform to reduce computational time. Thus, our HNN method is scalable with high-performance GPU computing on Cloud to screen billions of diverse compounds rapidly.The HNN is complex when applied with a very large number of parameters for CNN and FFNN. As a result, the HNN method suffers from higher variance in comparison to other machine learning methods such as RF, which are visible in the box plots in


Fundamental studies ceaselessly reveal how biomolecules interact with each other to provide cells with structural and enzymatic pivotal characteristics. The term “interactome” firstly introduced in e.g., proteins, DNA, lipids, small ligands and ions, rather than only its genetic content and organization. Dissecting these interactions is crucial to understand not only cellular functions, but also the molecular basis of diseases and designing new therapies as well as to create new bio-based approaches to build-up functional, responsive materials at the nano- and microscale, as summarized for instance by The interactions between biomolecules often require flexible surfaces to perform many different tasks such as recognition, regulation and signaling. These interactions rely on sophisticated, dynamic molecular mechanisms that must be tightly controlled within very crowded environments, such as cellular compartments, to allow tailored and tuned complexes, i.e., the layer of biomolecules, mainly proteins, adsorbing to the surfaces of nanoparticles (NPs), must be considered, and addressed as an essential component of nanomedicine designs and it is paramount to achieve a better understanding of the interaction between NPs and the specific biological environment they are exposed to.Many representative examples about how biomolecular interactions drive cells’ life can be recalled. It is now clear, for instance, that multivalent interactions may trigger the assembly of the highly dynamic and reversible membrane-less organelles via liquid-liquid phase separation or LLPS as shown by The importance of understating the molecular interactions between biomolecules is reflected by the explosive trend of several tools and techniques occurred recently. The last years, for instance, have seen the breakthrough of the deep learning neural network architecture-based methods for protein structure prediction (AlphaFold2) developed by This Research Topic pointed out special emphasis on the characterization of the interactions between molecular partners forming complexes, be it fully biological of hybrid. The contributions received are briefly described herein.Dubackic et al. the authors investigate the properties of α-Synuclein fibrils to aggregate into Lewis bodies by small angle neutron scattering (SANS). Lewis bodies are strongly associated with Parkinson’s and several other neurodegenerative diseases. The study examines α-Synuclein’s clusters formed under mild acidic conditions and with a wide range of different lipids mimicking different membrane systems. The main outcome of this work is that the clusters can be described as mass fractal aggregates whose dimensions may be reproduced using a simple model of rigid-rod clusters.In the original article by La Manna et al. focuses on the role of SOCS proteins  as putative source of protein-protein interaction inhibitors (PPI) of the signaling pathway JAK-STAT . In particular, they show the power of peptido-mimetic compounds in modulating JAK-STAT interactions in inflammatory diseases and in cancer onset. In both cases peptides derived from specific SOCS are able in cellular models to down-regulate the pathway and hence revert to a physiological state. Therefore, tailor-made peptides might be designed and used to specifically target only some subsets of isoforms in order to avoid general side effects.The review by De Lauro et al. presents a software written to optimize therapeutic antibodies (Ab) by designing complementary interfaces with the antigen (Ag) to be neutralized. As a proof of concept, they applied the design of tailored Ab-Ag interfaces to SARS-CoV2 spike protein. In particular, they have taken into account specific Ag with and without glycosylation and Ag which were not affected by known mutations at the time of the research. They compare their results with known protein-protein docking softwares and discuss their outcomes.The original research paper by via non-covalent interactions and interact via nanomaterials in such a way to drive their controlled aggregation into new, hybrid and responsive structures. In this context, the review proposed by Passaretti reports on many examples where biomolecules such as DNA and proteins or even whole organisms such as viruses are used to assemble graphene and graphene oxide materials with geometrical architecture including fibers, sponges and films which are found beneficial in many areas, e.g., energy storage, sensors, catalysis, air and water filtering as well as tissue engineering.When reporting on biological interactions and their use for practical purposes, bionanotechnology is currently being considered a flourishing area. Biomolecules, especially those forming supramolecular complexes, exhibit chemical features which are really wanted when building materials: they can self-assemble Due to the growing interest in the last few years towards materials based on biological building-blocks for many applications, such as plasmon-enhanced next-generation sequencing, biomolecular detection and biosensing, this Topic aims to provide new ideas for both fundamental and applied science at a molecular level.


The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts the 3D structure of two thirds of the human proteome. Its predictions can be used directly as structural models or indirectly as aids for experimental structure determination using X-ray crystallography, CryoEM or NMR spectroscopy. Nevertheless, AlphaFold 2 can neither afford insight into how proteins fold, nor can it determine protein stability or dynamics. Rare folds or minor alternative conformations are also not predicted by AlphaFold 2 and the program does not forecast the impact of post translational modifications, mutations or ligand binding. The remaining third of human proteome which is poorly predicted largely corresponds to intrinsically disordered regions of proteins. Key to regulation and signaling networks, these disordered regions often form biomolecular condensates or amyloids. Fortunately, the limitations of AlphaFold 2 are largely complemented by NMR spectroscopy. This experimental approach provides information on protein folding and dynamics as well as biomolecular condensates and amyloids and their modulation by experimental conditions, small molecules, post translational modifications, mutations, flanking sequence, interactions with other proteins, RNA and virus. Together, NMR spectroscopy and AlphaFold 2 can collaborate to advance our comprehension of proteins. In 1961, Anfinsen demonstrated that what determines the three dimensional structure of a protein is encoded in its amino acid sequence . This raOne key insight into how proteins fold was provided by Levinthal, who realized that if protein folding were to occur by a random sampling of conformers, then even the folding of a small protein would require more time than the age of the Universe . NeverthWith the development of accurate energy functions for protein folding, it became possible to directly simulate the folding process of very small proteins using molecular dynamics methods and a special purpose, massively parallel computer chip . This suIn contrast to these physicochemical based methods, other scientists sought to use a more biological approach based on garnering structural inferences from evolutionarily related protein sequences . This leFurther advances have come from the field of protein design, which has produced new folds with novel enzymatic functions . The reshttps://alphafold.ebi.ac.uk. High confidence predictions are reported for two-thirds of the proteome. While few percent of the regions or proteins predicted with low/very low confidence by AlphaFold 2 may be novel folds; the great majority corresponds to intrinsically disordered regions. The AlphaFold 2 output for a representative protein, TDP-43, with contrasting high confidence, folded domains and low confidence, disordered regions, is shown in Since 1994, a community experiment called “CASP”  has provided a proving ground for algorithms trying to solve the protein folding problem . Researc≤  1Å . In CASP≤  1Å . However≤  1Å . A furth≤  1Å . This ha≤  1Å  at httpshttps://blanco.biomol.uci.edu/upstruc), which was used to train AlphaFold 2, be sufficient for the program to capture concepts about their structures? Also, could AlphaFold 2 handle mobile protein regions which extend beyond the membrane proteins into the aqueous phase? Moreover, in a recent elucidation of a novel membrane protein called ChRmine, some structural features were reported to be mispredicted by AlphaFold 2 . Fersht notes AlphaFold 2 is an especially impressive achievement considering that for games like chess or Go, it was possible to program the rules, however for protein structure, AlphaFold 2 had to infer indirectly the rules from the protein sequences and structures. Fersht noted that while some professional chess players retired in the face of superior ML chess programs such as Alpha Go Zero, others use these programs as tools to better their game. Fersht proposes that we proteinologists follow their example and use AlphaFold 2 as a tool for protein design.A. Fersht compared and summarized Machine Learning (ML) programs for chess and Go to AlphaFold 2 . CompareThis thread is continued by D. Woolfson , who poiM. K. Higgens addressed how and whether AlphaFold 2 can help advance structural biology questions related to pandemics such as SARS-CoV-2 . HiggensAbout a third of the human proteome is predicted with low or very low confidence by AlphaFold 2. K.M. Ruff and R. V. Pappu  pointed In their articles, B. Strodel  and S. VOther computational approaches can also be applied to extend AlphaFold 2 structural models. For example, Normal Mode Analysis, which treats a protein structure as an oscillating system moving sinusoidally about a ground state, is computationally inexpensive and can reveal the effects of ligand binding and allosteric states . By contProtein electrostatics is another important area where AlphaFold 2 has been combined with another method to achieve advances. Alone, AlphaFold 2 does not predict the pKa or charged state of the titratable residues like Asp, His or Glu. This information is very important for assessing electrostatic interactions, solubility and binding to macromolecules, substrates and drugs, but experimental pKa measurements are generally laborious . Fortunavide infra), or other computational tools such as RoseTTAFold developed by D. Baker and his laboratory  from SARS-CoV-2 , or mode methods , are matWhereas X-ray crystallography and CryoEM obtain data on proteins in highly non-physiological conditions; namely extremely low temperatures and/or trapped inside crystals, NMR spectroscopy can provide high resolution structural data under near physiological conditions of pH, concentration and temperature. Therefore, as pointed out by M. Zweckstetter, it is important to use NMR spectroscopy to assess how accurately AlphaFold 2 structural models represent the structure of proteins in solution . For thr1HN, 15N, 13Cα, 1Hα, 13C, and 13Cβ nuclei to the flexibility calculated from the protein structure using mathematical rigidity theory  compared to NMR (8%). The very cold temperatures typically used by X-ray crystallography rigidify protein structures , especiay theory . They foTaken together, these commentaries suggest there is some broad agreement on the AlphaFold 2’s strengths, its weaknesses and the new horizons it opens up, which are summarized in In the previous paragraphs, ways that AlphaFold 2 could advance NMR spectroscopy and other structural biological methods were described. Regarding the weaknesses of AlphaFold 2, some of them, such as the prediction of protein complexes and the prediction of highly unusual folds, may well be overcome in the next few years. However, four weaknesses seem to be more difficult for AlphaFold 2, or ML in general, to overcome. These are: 1) the prediction of folds that populate only a small fraction of a conformational ensemble, 2) the impact of post translational modifications, 3) the prediction of interaction with ligands and 4) the prediction of partially populated structure in intrinsically disordered proteins. Fortunately, these problems can be addressed using NMR spectroscopy.++-bound forms of calmodulin  exchange provided the first way to detect and characterize sparsely populated intermediates forming during protein folding . H/D excIn the last 15 years, a new series of NMR experiments have been developed, i.e., dark state exchange saturation transfer, Carr-Purcell-Meiboom-Gil relaxation, chemical exchange saturation transfer, paramagnetic relaxation enhancement, which reveal conformational populated to a few percent forming under different time scales  as well Post translational modifications (PTM) are quite common in proteins; over 10% proteins are phosphorylated and another 10% plus are glycosylated . PTMs arThe pharmaceutical industry uses protein structures as a starting point for screening compound libraries to identify ligands as lead compounds for drug development. The enormous increase in accurate protein structures provided by AlphaFold 2 will certainly stimulate these efforts, however, enzyme active sites tend to “break the protein folding rules” and have unusual conformations . This ca1H NMR experiments can be performed on a mixture of several binding candidate molecules in the presence of dilute, unlabeled protein. These experiments detect changes in the chemical shift and resonance width of the ligands or in their dynamics, as a compound bound to a protein will show faster transverse (T2) relaxation, or in their diffusion rate, since a small molecule united to a larger protein will diffuse more slowly  which are typically observed in NMR studies of proteins, two other spin ½ nuclei, 19F and 31P are useful for binding studies. Due to its very wide range of chemical shift values and the sensitivity of its resonance to broadening upon interaction, 19F has been incorporated into chemical libraries to facilitate the screening of small drug-like “fragment” compounds , ligand binding or partially structured zones in intrinsically disordered proteins (IDPs). Fortunately, these shortcomings of AlphaFold 2 are strengths of NMR spectroscopy. In the future, results from NMR spectroscopy and other experimental methods could pave the way for future ML methods able to predict sparsely populated conformations, the effects of PTM, small molecule binding and preferred conformations in IDPs.


The structural information of a protein is pivotal to comprehend its functions, protein–protein and protein–ligand interactions. There is a widening gap between the number of known protein sequences and that of experimentally determined structures. The protein structure prediction has emerged as an efficient alternative to deliver the reliable structural information of proteins. However, it remains a challenge to identify the best model among the many predicted by one or a few structure prediction methods. Here we report ProFitFun-Meta, a neural network based pure single model scoring method for assessing the quality of predicted model structures by an effective combination structural information of various backbone dihedral angle and residue surface accessibility preferences of amino acid residues with other spatial properties of protein structures. The performance of ProFitFun-Meta was validated and benchmarked against current state-of-the-art methods on the extensive datasets, comprising a Test Dataset , an External Dataset , and CASP14 Dataset (n = 1200). The comprehensive performance evaluation of ProFitFun-Meta demonstrated its reliability and efficiency in terms of Spearman’s (ρ) and Pearson’s (r) correlation coefficients, GDT-TS loss (g), and absolute loss (d). An improved performance over the current state-of-the-art methods and leading performers of CASP14 experiment in quality assessment category demonstrated its potential to become an integral component of computational pipelines for protein modeling and design. The minimal dependencies, high computational efficiency, and portability to various Linux and Windows OS provide an additional edge to ProFitFun-Meta for its easy implementation and applications in various regimes of computational protein folding. Depending on the approach used and availability of computational resources, the number of decoys may vary from hundreds to thousands The recent development in high-throughput next-generation sequencing techniques and in contrast to the time-consuming experimental methods for protein structure determination has widened the gap between experimentally solved protein structures and known protein sequences Primarily, the methods for protein structure quality assessment employ-three approaches, either individually or in combination, viz. pure single model, quasi single model and clustering based assessment approach Over the years, the scientific community has witnessed the development of several state-of-the-art methods for assessing the decoys to distinguish the most suitable model structures with a reasonable accuracy. The assessment from recent CASP experiments revealed the superior accuracy of consensus approaches that integrate complementary algorithms/features to ameliorate the estimations of model accuracies. The explicit suitability of a model structure is dictated by its similar features and properties to mimic native or native-like proteins 2The ProFitFun-Meta was developed by using structural features from the non-redundant datasets of experimentally solved protein structures and predicted model structures of diverse quality. In general, the structures under 3 Å resolution  deliver the structural information of side chain orientations of amino acid residues in the protein structures and can be regarded as excellent in terms of quality of structures. Likewise, the structure around 5 Å resolution  can provide the structural information of the overall fold and secondary structural arrangement in the protein tertiary structure and can be considered as good (model) structures. The structural information furnished by the structures beyond 6 Å  is very limited and thus can be considered as poor in terms of quality of structures. Keeping the extent of structural information available at different resolutions and the rationale of maintaining the structural diversity in terms of quality, a non-redundant initial dataset was compiled from excellent quality experimentally solved protein structures, and variable quality  predicted model structures, as described below.2.1https://predictioncenter.org). For the CASP experiment, multiple model structures (referred as ‘decoys’) that are predicted through different protein structure prediction methods are available for individual protein sequence (referred as ‘protein target’). The predicted model structures  in the compiled initial dataset represented 392 protein targets with an average of 205 (±18) model structures for an individual protein. A summary of the distribution of the GTD-TS  for the predicted model structures  is shown in A non-redundant dataset of experimentally solved high resolution (up to 3 Å) globular protein structures was compiled at 90 % sequence identity from Protein Sequence Culling Server (PISCES), resulting in 8041 protein structures 2.2The compiled dataset of experimental protein structures and predicted model structures was divided into training and test datasets. Out of 8041 high resolution experimental protein structures, 6030 protein structures were included in the Training Dataset and 2011 protein structures were considered as the Test Dataset. Among the predicted model structures  of 392 protein targets from CASP9 to CASP14, the model structures  corresponding to 294 protein targets were included in the Training Dataset and model structures  corresponding to 98 protein targets were allocated to the Test Dataset. Altogether, the Training Dataset comprised of 69,243 protein structures  and the Test Dataset comprised of 26,604 protein structures .2.3α geometry outliers, the normalized clash score per 100 residues, and the overall molprobity score. The three features acquired from Procheck included the G-scores for the dihedral angles, the covalent interactions, and the overall G-score. Notably, all the features integrated in ProFitFun-Meta were derived in pure single model mode. The scores for the selected features were calculated for the compiled datasets of experimental and predicted protein structures. The Pearson correlation of individual features is summarized in supplementary For the development of ProFitFun-Meta, the sequence and structure based features from three different quality assessment methods were integrated, namely ProFitFun 2.4A neural network model, ProFitFun-Meta, was developed by implementing a replicable training scheme on the Training Dataset  with a maximum number of 500 iterations through a multi-layer perceptron algorithm with a backpropagation approach having 5 fully connected hidden layers of 100 neurons . The com2.5The prediction accuracy of ProFitFun-Meta was evaluated on the Test Dataset , comprising a completely exclusive dataset of experimentally solved protein structures  and model structures . Additionally, the evaluation of ProFitFun-Meta was performed on an external dataset of 40,000 model structures corresponding to 200 non-homologs proteins 2.6Pred - GDTObs|d = |GDT). The GDT-TS loss (g) reflects the measure of error by which the method misses the best model available in the pool of decoys for a protein target (TR – GDTBM|g = |GDT). The TRGDT is the predicted GDT-TS of the top ranked model by the used method and BMGDT is the actual GDT-TS score of the best model available in the evaluated model structure of a protein. Notably, this metric can be implemented in case of rank ordering different models  for the same protein.For the performance evaluation and benchmarking of ProFitFun-Meta, the evaluation metrics were adopted from the CASP experiments 3The evaluation of the ProFitFun-Meta for the prediction of GDT-TS was performed on the basis of the statistics on the Test Dataset  and an External Dataset .3.1The ProFitFun-Meta was initially evaluated on the basis of its performance in the cross validation (10-fold) and the prediction statistics on the Test Dataset. In the cross validation, the ProFitFun-Meta performed exceptionally well with a Pearson’s correlation coefficient (r) of 0.88 (RMSE = 0.103). A similar result could be replicated when the ProFitFun-Meta was evaluated on the Test Dataset and a slight decline in the performance was observed . Commonly, the machine learning based trained models tend to underperform when evaluated on a never seen dataset as compared to their performance in cross validation. In the case of ProFitFun-Meta, the Test Dataset was completely exclusive at protein sequence and structural levels as homologous sequences/structures were deliberately eliminated. The performance of ProFitFun-Meta was benchmarked against different state-of-the-art methods on the basis of Pearson’s (r) and Spearman’s (ρ) correlation coefficients, absolute loss (d) and GDT-TS loss (g) metrics, where the high values Pearson’s (r) and Spearman’s (ρ) correlation coefficients indicate a better efficiency of the method to identify and quantify the quality of protein model structures. Likewise, the lower values of absolute loss (d) and GDT-TS loss (g) metrics reflect better performance.In the case of experimental structures  in the Test Dataset, only absolute loss (d) from the predicted and observed GDT-TS could be calculated (0.09 ± 0.062). The Pearson’s correlation coefficient could not be calculated as the GDT-TS for all the experimental structures was the same (i.e. 1.0). While the Spearman’s correlation coefficient (ρ), GDT-TS loss (g) and Z-scores could not be calculated due to unavailability of multiple models for the same protein. The performance of ProFitFun-Meta and its benchmarking with other state-of-the-art methods on the Test Dataset  is summarized in The ProFitFun-Meta demonstrated an improved performance in terms of Pearson’s correlation coefficient (r = 0.75), Spearman’s correlation coefficient (ρ = 0.70), absolute loss (d = 0.128), and GDT-TS loss (g = 0.132) over all other methods used in the benchmarking. The ProFitFun-Meta performed significantly better over its antecedent ProFitFun , that indicated the complementary effect of additional features in ProFitFun-Meta in achieving better accuracy for predicted GDT-TS scores of protein model structures. The significance of ProFitFun-Meta performance was statistically analyzed by a two-sample Kolmogorov-Smirnov test. The statistical analysis supported the significant difference  in performance of ProFitFun-Meta over other methods used in the benchmarking, as summarized in supplementary 3.2A target-wise evaluation of ProFitFun-Meta was performed on a structurally diverse External Dataset of 40,000 model structures of 200 non-homologous proteins, generated through 3DRobot The ProFitFun-Meta method demonstrated an improved overall performance as compared to all the other quality assessment methods used for the benchmarking. The improvement in the performance of ProFitFun-Meta over its predecessor, ProFitFun, could be attributed to the addition of new features.3.3The benchmarking of the ProFitFun-Meta was further extended with the leading state-of-the-art methods in CASP14, as evaluated by the CASP assessors in the single models QA category.https://predictioncenter.org/casp14/doc/presentations/2020_12_03_EMA_Assessment_Seok.pdf). The details of these methods can be accessed from the abstract book of CASP14 proceedings available at https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf. Out of total 34 protein targets for which the native structures are available in the public domain, the model structures (n = 3538) of 25 protein targets were part of the Training Dataset, the model structures (n = 1200) for another 8 protein targets were part of Testing Dataset . Therefore, we only extracted the performance metrics of ProFitFun-Meta for these 8 proteins from the Testing Dataset, and that of the leading state-of-the-art methods for the same set of proteins from the CASP14 experiment, https://predictioncenter.org/casp14/results.cgi. The performance benchmarking of ProFitFun-Meta with the leading QA methods of CASP14 experiments is summarized in , followed by BAKER-ROSETTA , tFold-IDT , and VoroCNN-GDT . Notably, the performance of leading methods in CASP14 quality assessment category was falling within a narrow ranges of Pearson’s correlation coefficient (r = 0.33 to 0.42) and Spearman’s correlation coefficient (ρ = 0.35 to 0.43). It was observed that the ProFitFun-Meta method performed considerably better in terms of Pearson’s correlation coefficient (r), Spearman’s correlation coefficient (ρ), absolute loss (d), and GDT-TS loss (g) as compared to the other methods on the CASP14 dataset.3.4ProFitFun-Meta requires 8–10 min to evaluate 100 model structures of a protein with 300 residues on a single processor. ProFitFun-Meta can be implemented in both the command line mode (for Linux OS based users) or the graphic user interface (for Windows OS based users). Further, ProFitFun-Meta is freely available at http://github.com/KYZ-LSB/ProFitFun-Meta for standalone installations. A detailed and easy to follow user manual for installation requirements and procedure is included therein. MolProbity and Procheck are not distributed with the standalone version of ProFitFun-Meta due to license related restrictions and can be obtained from the respective developers. However, details related to their download and installation are provided for the convenience of the users.4The recent innovative methodological developments in the field of protein structure prediction are mainly by the virtue of various machine learning based approaches. The field of computational protein structural modeling and protein design have witnessed the implementation of different flavors of machine learning approaches to deliver a remarkable success. The revolutionary success of some advanced deep-learning based state-of-the-art methods has demonstrated a promising potential of computationally generated protein structural information in substantiating its impact in computer aided drug discovery, computational protein design and protein functional characterization. The success of protein structural modelling and computational protein design depends upon the reliable protein structure quality assessment. However, the accuracy estimation of the predicted model structure has not yet been consummated adequately. The proposed method here, ProFitFun-Meta, is intended to serve the protein structure biologists in optimally employing and comprehending the predicted protein structural information. We integrated the structural information of experimentally characterized proteins, in terms of backbone dihedral angles and surface accessibility preferences of residues, spatial properties of protein structures for quality estimation , and potential of model structures to mimic the experimentally solved protein structures by accounting for the deviations of covalent interactions, dihedral angles and overall interactions. These parameters were unified together through a neural network based machine learning model, ProFitFun-Meta, to deliver a highly precise quality evaluation of the protein model structures. The accuracy of ProFitFun-Meta is validated on an extensive dataset of ∼ 65,000 model structures of non-homologous proteins.A better knowledge of structural and functional features of proteins is pivotal to various therapeutic developments in identifying novel drug targets. ProFitFun-Meta can furnish a reliable quantification of the structural quality of the predicted model structures that are generated through different protein structure prediction methodologies. For instance, the reliable scoring of model structures can expedite an optimal utilization of structural models for computer aided drug discovery regimes, ligand binding studies, structure to function characterization, and various protein–protein interaction and mutagenesis studies. The accuracy of ProFitFun-Meta can be easily harnessed by incorporating it into the computational pipelines for protein modeling and protein design. The minimal additional dependencies, high time and computational efficiency, and the automated standalone source codes optimized for portability to various Linux and Windows OS provide an additional edge to ProFitFun-Meta for its potential applications in various regimes of computational protein design, computational protein folding and function annotation, and computer aided drug designing for the scientists even without computational biology and bioinformatics expertise.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Our results show that AlphaFold produce extremally reliable protein folds, but in many cases is unable to predict high-resolution protein complexes accurately. Nonetheless, the results show that AlphaFold can revolutionize the investigation of these proteins, particularly by allowing high-throughput scanning of protein structures. Meanwhile, we show that the AlphaFold results need to be validated and should not be employed blindly, with the risk of obtaining an erroneous protein mechanism.Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how mechanoactive proteins sense and respond to mechanical forces. However, even SMFS has its limitations, including the lack of detailed structural information during force-loading experiments. That is where molecular dynamics (MD) methods shine, bringing atomistic details with femtosecond time-resolution. However, MD heavily relies on the availability of high-resolution structural data, which is not available for most proteins. For instance, the Protein Data Bank currently has 192K structures deposited, against 231M protein sequences available on Uniprot. But many are betting that this gap might become much smaller soon. Over the past year, the AI-based AlphaFold created a buzz on the structural biology field by being able to predict near-native protein folds from their sequences. For some, AlphaFold is causing the merge of structural biology with bioinformatics. Here, using an  Over the past year, the artificial intelligence (AI)-based software AlphaFold created a buzz on the structural biology field. For the first time, a software was able to predict near-native protein folds from their genetic sequence . DeepMinSaccharomyces, and E. coli  simulations are a unique tool to investigate biomolecules’ behavior with atomic resolution . Howeverin silico single-molecule force spectroscopy (in silico SMFS)  . In thisco SMFS) , titin (co SMFS) , human fco SMFS) , aquaporco SMFS) , among oin silico SMFS methodology, allowed us to go even further and to fine-tune mechanical properties of protein folds  is shown at The per-residue model quality can be evaluated by the predicted Local Distance Difference Test (pLDDT) quality scores, standard metric to evaluate AlphaFold generated models. In our studies, the pLDDT scores varied from ∼20 to 90  ranging Staphylococcal adhesins use a conserved “dock, lock, and latch” (DLL) mechanism—in which the host target, usually a peptide on the order of 15 residues, is first bound (dock), then buried (lock) between two immunoglobulin-like (Ig) fold domains N2 and N3 , clumping factor A (ClfA), and SdrE.Here, we tested the reliability of peptides . AlphaFopeptides  interfacpeptides , keepingpeptides . DetailsThe predicted complexes were evaluated using pLDDT scores . Most ofp < 0.005) between the model stability and the AlphaFold Multimer scores. Therefore, we can anticipate that high-scored structures present less deviation from its initial configuration, suggesting a more stable or resilient fold. AlphaFold Multimer scoring is based on an ipTM score that takes into account protein-protein interactions. This scoring function was shown to be more advantageous over the pTM and pLDDT scores used in AlphaFold 2  seen for some of the complexes might have arisen from inaccurate initial structures. Visual inspection of the models with low rupture forces revealed that in most cases the locking strand was modelled in an unfavorable conformation to hold the peptide in the DLL configuration, which explains the observed behavior   .However, as nearly any other AI-based tool, AlphaFold is biased towards its training set, meaning that the search for unusual folds is unlikely to provide an accurate result. Despite the software’s success on the folded part of most proteins, AlphaFold lacks accuracy for regions where fewer sequences are available for alignment and intrinsically disordered regions, the latter are about one third of the human proteome, present in all proteomes of all kingdoms of life, and of all viral proteomes analyzed so far . It alsoStaphylococcus adhesins and estimate how stable are the protein structures. Ignoring the disordered regions, AlphaFold was able to model the Ig-like domains of MSCRAMMs adhesins as well as other key structural features of these proteins, such as the homologous B domains, for all the tested sequences. With a little refinement from in-equilibrium MD simulations, the generated structures could help to investigate the properties of many of the domains that still have an unknown function.In order to show how revolutionary AlphaFold is for the single-molecule biophysics community, here we put AlphaFold to the test by using it to model full length S. aureus involved in biofilm formation. These protein complexes have been shown to be mechanically hyperstable, with a force resilience equivalent to that of a covalent bond at different pulling velocities (Additionally, we tested the newly developed AlphaFold Multimer to model adhesin:peptide complexes from different strains of locities , and liflocities . By compIn summary, AlphaFold 2 is a truly revolutionary tool that is bringing a new level of structural biology to bioinformatics. Although there are many areas where its methodology can be improved, the current algorithm can be clearly employed to work alongside single-molecule biophysics experiments. It is important to note that, as any other scientific tool, particularly new ones, AlphaFold 2 results cannot be employed blindly. Assessing the quality of the results and the usability of the predicted structures to infer function or mechanism to proteins is still the work of a trained scientist that can bring together data from multiple sources in a careful analysis of protein structure and dynamics.


In pancreatic cancer, the CaSR limits cell proliferation. TCP-related four novel missense mutations P163R, I427S, D433H and V477A, found in CaSR extracellular domain (ECD) protein, which were reported in the mutTCPdb Database (https://lms.snu.edu.in/mutTCPDB/index.php). P163R mutation occurs in ligand-binding domain 1 (LBD-1) of the CaSR ECD. To investigate the influence of these variations on protein function and structural activity multiple in-silico prediction techniques such as SIFT, PolyPhen, CADD scores, and other methods have been utilized. A 500 ns molecular dynamic simulation was performed on the CaSR ECD crystal structure and the corresponding mutated models. Furthermore, Principal Component Analysis (PCA) and Essential Dynamics analysis were used to forecast collective motions, thermodynamic stabilities, and the critical subspace crucial to CaSR functions. The results of molecular dynamic simulations showed that the mutations P163R, I427S, D433H, and V477A caused conformational changes and decreased the stability of protein structures. This study also demonstrates the significance of TCP associated mutations. As a result of our findings, we hypothesised that the investigated mutations may have an effect on the protein’s structure and ability to interact with other molecules, which may be related to the protein’s functional impairment.Tropical Calcific Pancreatitis (TCP) is a chronic non-alcoholic pancreatitis characterised by extensive calcification. The disease usually appears at a younger age and is more common in tropical regions. This disease’s progression can lead to pancreatic diabetes, which can subsequently lead to pancreatic cancer. The CASR gene encodes a calcium-sensing receptor (CaSR), which is a GPCR protein of class C. It is expressed in the islets of Langerhans, the parathyroid gland, and other tissues. It primarily detects small gradients in circulating calcium concentrations and couples this information to intracellular signalling, which helps to regulate PTH (parathyroid hormone) secretion and mineral ion homeostasis. From co-leading insulin release, CaSR modulates ductal HCO Inflammation of the pancreas is referred to as pancreatitis. Pancreatitis can be classified as acute or chronic . TropicaB, and Taste 1 receptors  secretion and mineral ion homeostasis .3- secretion and Ca2+ concentration in pancreatic juice. CaSR could stimulate cell-cell communication, β-cell proliferation, and intracellular Ca2+ release to co-lead insulin secretion. CaSR also inhibits cell proliferation in pancreatic cancer, and CaSR mutations, with or without SPINK1, cause pancreatitis  binding promotes the inactive conformation with an open cleft between both protomers. In the active state, both protomers approach each other and form a closed conformation in the association of agonist binding . Since high intracellular calcium levels activate trypsinogen within acinar cells, which results in pancreatic autodigestion and pancreatitis . CaSR mu binding . CaSR cod in ECD .2+ or Mg2+ are required for full activation, but the presence of metal ions alone can promote receptor activation  play a significant role in phenotypic changes when they occur in the coding DNA sequence of a biologically important gene, resulting in a non-synonymous missense effect and a single amino acid change in the protein sequence of the respective gene .A molecular dynamics simulation study of protein can provide insight into the details of atomic behaviour of protein upon mutation in protein structure . As a reThis study investigated the effect of CASR gene SNVs associated with TCP on protein structure and function. These SNVs leads to mutations in protein sequence such as P163R, I427S, D433H, and V477A .SIFT, PolyPhen, CADD, PROVEAN, I-Mutant, and PANTHER, along with other methods, were used to predict the impact of SNV on structure and function. Using these methods, all four mutations predicted to have a significant impact on protein structure and function. To investigate the atomic-level details and subsequent impact of SNVs on structure and functions, 500 ns molecular dynamics simulations of WT and mutant proteins were performed. In addition, essential dynamics analysis for MD simulation trajectories was performed to investigate the most concerted motion of the protein as well as the effect of the mutation.Based on the analysis we can summarize, out of P163R, I427S, D433H, and V477A mutations P163R is destabilizing and having significant impact on structure and function. This mutation significantly impact protein structure evaluated using various in silico measures, MD simulation, and essential dynamics analysis. However, MD simulation studies of all mutations also showed deviations from WT stability and dynamics.https://lms.snu.edu.in/mutTCPDB/index.php) (The CASR SNV data associated with TCP were retrieved from the mutTCPdb database (dex.php) . This dadex.php) .The non-synonymous SNP analysis was carried out to predict mutation’s functional impact using various in-silico methods and servers. These are categorized into sequence based, structure based, and pathogenecity based methods.https://cadd.gs.washington.edu/snv).CADD (combined annotation dependent depletion) is a web-based server that predicts the deleteriousness of variants across the human genome . Using tSIFT is an algorithm that predicts whether amino acid substitution will affect protein function based on amino acid sequence homologies and physical properties. A SIFT score < 0.05 is predicted to be harmful. A substitution with a score ≥ 0.05 is predicted to be tolerated. PHRED score is a scaled C-score of ≥10, indicating that these are predicted to be the 10% most deleterious substitutions that can be made to the human genome. A score of ≥20 denotes the 1% most deleterious mutation, and so on. PolyPhen is a tool that predicts the possible impact of amino acid substitution on the structure and function of a human protein based on physical and comparative considerations. It predicts whether the substitution is “probably damaging”, “possibly damaging”, or “benign”.http://provean.jcvi.org/). PROVEAN’s capacity to predict single amino acid alterations was compared to that of the existing tool CADD in this study . A PROVEAN value of 2.5 or higher is regarded neutral, whereas a score of less than 2.5 is considered detrimental for a nsSNP  is a web-based tool that predicts whether an amino acid substitution affects the biological function of the CaSR protein ( a nsSNP .http://structure.bmc.lu.se/PON-P2/) tool predicts the pathogenicity of amino acid substitutions; it is a machine learning-based classifier for the classification of amino acid features on human proteins. PON-P2 divides amino acid variants into three categories: pathogenic, neutral, and unknown. It also makes use of Gene Ontology (GO) annotations, evolutionary conservation, and, if available, annotations of functional sites. This tool can analyse various formats of nsSNP and is easily accessible via amino acid substitution(s) and one of Ensembl gene identifiers, UniProtKB/accession ID, for identifier submission (The PON-P2 (bmission .http://www.pantherdb.org/about.jsp) is a protein classification system designed to facilitate high-throughput analysis by classifying proteins (and their genes). It predicts the likelihood that a nsSNP will have a functional impact on the protein. It calculates the length of time (in millions of years) for a given amino acid that has been preserved to find the lineage leading to the protein of interest, and the longer the preservation time, the greater the likelihood of functional impact. PANTHER-PSEP is the name of the method (position-specific evolutionary preservation)  (tion) H. .The I-Mutant is used to predict the stability changes upon mutations . It is bhttp://biosig.unimelb.edu.au/duet/) uses an integrated computational approach to study the effect of mutation (nsSNP) on protein stability in humans and other genomes. DUET uses two complementary approaches, mutation Cutoff Scanning Matrix (mCSM) and Site-directed mutation (SDM), in a consensus prediction. It integrates both the scores using support vector machine (SVM). The combined value is in the form of the actual free energy value ΔΔG. The input for the DUET web server is the PDB structure file or 4-letter PDB code along with single point mutation or systematic mutation. This tool gives the DUET score and SDM and mCSM score in the result. mCSM and SDM2 were used to study the protein stability of the effect of mutation (DUET (mutation .http://cupsat.tu-bs.de/) is a web server to predicts impact of point mutations on protein stability. The model can distinguish between secondary structure specificity and amino acid environment based on its solvent accessibility. The tools predict mutant stability by combining PDB structure with existing and new protein structures  (ructures .https://zhanglab.ccmb.med.umich.edu/STRUM/) predicts stability changes caused by non-synonymous single-point mutations (nsSNP). It uses a boosting algorithm to predict the fold stability change (G) of protein molecules when they are mutated (The STRUM web server ( mutated .https://loschmidt.chemi.muni.cz/predictsnp/) is a Consensus classifiers tool for predicting disease-related mutations. It provides easy access to other tools for the consensus classifier PredictSNP as well as annotations from the Protein Mutant Database and the UniProt database. To predict pathogenicity, it uses data in fasta format. Other measures, such as MAPP, PHD-SNP, and others, are also available through this server (PredictSNP (s server .REVEL (rare exome variant ensemble learner) is an ensemble method for predicting the pathogenicity of missense variants that is based on individual tools such as MutPred, FATHMM, PolyPhen, VEST, SIFT, MutationAssessor, PROVEAN, MutationTaster, LRT, SiPhy, GERP, phyloP, and phastCons. To identify pathogenic variants among a list of rare variants, pre-computed REVEL scores were provided for all possible human missense variants. The REVEL score assigned to a single missense variant can range from 0 to 1. Higher scores indicate that the variant is more likely to be pathogenic .http://consurf.tau.ac.il) is a tool for analysing the evolutionary pattern of macromolecule amino acids or nucleic acids to determine the region important for structure and function. ConSurf web server analyses sequence or structure input and gathers homologs, infers multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relationship. The alignment can be done with MAFFT-L-INS-I, PRANK, Muscle, or Clustalw. The conservation scores for each residue were calculated using Bayesian methods or Max. Likelihood (ML). Consurf prediction scores range from 1 to 9, with 1 indicating a variable region, 5 indicating an intermediate conserved region, and 9 indicating a highly conserved region . Multiple sequence alignment was performed for WT and mutant protein sequences using multiple sequence viewer panels of Schrodinger’s maestro.2+ and co-agonist tryptophan derivatives  bound with Mgivatives . The Swiivatives  was usedivatives . The proivatives .Desmond (version 2020–1) was used to simulate the molecular dynamics of the WT and mutant proteins. The TIP3P solvation model was used to create the systems for molecular dynamics simulation. For each system, periodic boundary conditions were defined using a cubic box shape and the buffer method with a distance of 10 Å. Ions were strategically placed to neutralise the system. The For each system, the molecular dynamic simulation was run for 500 ns in triplicate. The energy was recorded every 1.2 ps, and the trajectory was saved every 100 ps. The volume of the box was equilibrated with the NPT ensemble at 300 K and 1.01325 bar pressure. Before selecting the simulation option, the systems were equilibrated using Desmond’s default relaxation protocol, i.e., Relax model system. The molecular dynamics simulation was performed with a 2 (fs) time step, Noose-Hoover chain temperature coupling (relaxation time 1.0 ps), and Martyna-Tobias-Klein pressure coupling (relaxation time 2.0 ps) of Isotropic style. The short-range method for coulombic interaction had a cutoff radius of 9.0.Desmond’s simulation event analysis programme was used to calculate various properties and measurements for all MD trajectory analyses. Root Mean Square Deviation (RMSD) provides information about the structural conformation of a protein throughout the simulation. It can indicate whether the simulation has equilibrated. The calculation of root mean square fluctuation (RMSF) assists in characterising the local changes or fluctuation around the amino acids in the protein chain. During MD simulation, the radius of gyration (Rg) indicates the compactness of the protein and its folding. Solvent Accessible Surface Area (SASA) is a key property of proteins that determines their folding and stability. Secondary Structure Elements (SSE) monitored throughout the simulation will aid in identifying the distribution of SSE by residue index throughout the protein’s structure. This analysis summarises the SSE composition for each trajectory frame over the course of the simulation. Hydrogen bonds (H-bonds) contribute to protein stability in a positive way. The number of hydrogen bonds in a protein indicates its stability. Salt bridges (SB) are involved in protein stability and folding, and changes in salt bridge contribution result in changes in protein stability.The essential dynamics analysis studies motions in the essential subspace, which has only a few degrees of freedom and is created by removing the overall translational and rotational motion because these are irrelevant for their internal motion. This returns atomic position expression values in a cartesian molecular coordinate system. This subspace describes the motions related to protein functions .The eigenvectors and eigenvalues were calculated using Schrodinger’s script “trj essential dynamics.py” with the default parameters, as well as the cross-correlation matrix plotted per-frame conformational deviations projected onto the calculated modes . Eigenvectors  represent positional deviations, and the magnitude of atomic fluctuations is associated with eigenvalues. As a result, the first principal component has the greatest positional deviation. The majority of the fluctuations are concentrated in a small subset of the few top PCs. Maestro and PyMol were used to visualise major protein motions. The impact of non-synonymous SNVs was evaluated using various in-silico tools, and the results are shown in The I-Mutant server predicted the differences in Gibbs free energy  between WT and mutant proteins. As a result, the mutation I427S had the greatest impact on structure stability, with a DDG value of -2.44 kcal/mol. The other three mutations also had an effect on structure, resulting in decreased stability with DDG values of -1.30, -0.32, and -1.70 kcal/mol for P163R, D433H, and V477A, respectively. As a result of the I-Mutant server prediction, all four mutations have decreased protein stability. Similarly, DUET and CUPSAT support the findings of I-mutant, indicating that these mutations have a significant impact on structure stability, as indicated by the DDG values in In addition to these prediction methods and tools, consurf analysis results show the conservation of the amino acid at positions P163 (score 8), I427 (score 6), and V477 (score 7), except for D433 (score 1). This means that any changes to the amino acids at these positions will result in structural and functional changes to the protein. P163, I427, and V477 amino acids are found in the buried region. In contrast, consurf results show that amino acid D433 is present in the exposed region .2+ ion with amino acid residues were Ile81, Ser84, Leu87, and Leu88.Analysis of crystal structure  revealedSer147, Ser168, Ser169, Ser170, and Glu297 had similar interactions with co-agonist. Additionally, binding site residues within a defined cutoff of 5.0 Å were displayed. Superimposed residues at mutation sites were shown for both wild type (WT) and mutants in Each amino acid has the physicochemical properties required for its structure and function. Thus, mutation of amino acids at specific positions results in a change in physicochemical properties. This change, however, may involve changes in size, charge, interactions, and other properties. In this case, the resultant residue increased in size, gained +1 charge, and decreased in hydrophobicity due to the mutation P163R. This mutation occurs in the protein’s ligand-binding domain 1 (LBD1), which may be involved in its functional activity. Changes at this position may affect the protein’s function. The D433H mutation causes an increase in size, and the WT residue is negatively charged, whereas the mutant is positively charged. In the case of V477A, there was a reduction in the size of the mutant residue, which may be involved in less interaction and thus be a cause of structural changes.The study of molecular dynamics simulation provides insight into the atomic details of protein structure and its dynamical behaviour when structure changes . The effThe RMSD analysis was used to examine the conformational changes of protein atoms in comparison to the reference frame, which provides an inside look at protein stability. The initial and final frames of the MD simulation were visualised and aligned using PyMol to calculate the RMSD for c-alpha atoms . This deIn addition, all WT and mutant initial and end frames were aligned separately . The RMSFor each of the five systems, triplicate average RMSD plots for proteins and protein-ligand complexes were created . The ProThis analysis revealed that upon mutations, average and SD of RMSD values were decreased compared to WT protein, leading to conformational changes. Mutation P163R shows the higher dgree of changes in RMSD measurements.RMSD plots of the Protein-Ligand complex  revealedThe characterization of local changes along the protein chain is referred to as RMSF. It computes RMSF using the individual amino acid contribution. During the simulation, it aids in the analysis of fluctuating or stable amino acids. The protein’s terminal and loop regions are extremely mobile. The impact of a specific amino acid mutation on protein structure was investigated and compared using RMSF analysis from the trajectories of all five protein systems . The aveProtein SSE were monitored throughout the simulation and summarised in the plot as a monitoring of each residue and its SSE assignment over time and SSE distribution by residue index throughout the protein structure . Table 3Protein compactness is measured using Rg values. When a protein maintains its fold stably, the value of Rg is relatively constant and low, and vice versa. It means that as a protein unfolds during the MD simulation, the Rg increases. The Rg data of WT and mutants were plotted and compared . The aveThe solvent accessible surface area (SASA) was measured to determine the effect of mutations on the folding or unfolding of the structure due to hydrophobicity changes. Hydrophobic residues are typically found in the protein’s core. The most advantageous space is the core due to solvent contact on the surrounding protein molecule. As a result, this phenomenon is important in protein packing or folding. The results of SASA analysis of WT and mutant  show thaIntramolecular hydrogen bonds (H-bonds) and salt bridges help proteins maintain their conformation. From MD simulation trajectories, we calculated the intramolecular H-bonds and salt-bridge of all proteins, WT, and mutants . The graph in A time-series analysis of protein-ligand interactions was performed using MD simulation trajectories, and a plot of amino acid residues interacting with the ligand was generated. The protein ligand interaction result depicts the interaction of a specific amino acid residue over the course of the simulation. In the case of multiple types of interactions possible by a residue, the interaction is considered a percentage (percent) value, which may be greater than 100 percent. In this study, essential dynamics analysis was performed using Schrodinger’s script, which was given the output of 10 PCs from MD simulation trajectories, as well as covariance data and a cross-correlation matrix for each WT and mutant protein P163R, I427S, D433H, and V477A. We measured the variance for each PC in WT and mutants  and founThe percent motion and percent aggregate motion of each PC  revealedX and Y axes, respectively, and the results were compared  from each WT and mutant P163R, I427S, D433H, and V477A were analysed because these PCs account for the majority of protein motion during simulation. PC1 and PC2 were plotted on the compared . This anCross-correlation analysis is useful for studying the dynamic behaviour of each amino acid in a protein chain in relation to other amino acids. For this, a cross-correlation matrix was generated for WT and mutant proteins using Schrodinger’s script  . We obseIn addition, for the first two major components, we generated the porcupine plot for WT and mutant proteins. The spikes originating from CA atoms in protein represent motion directionality with magnitude .In-depth analysis of the porcupine plot of WT PC1  revealed2+ concentration in pancreatic juice by triggering ductal electrolytes and fluid secretion. Hypercalcemia raises the risk of acute pancreatitis by causing early activation of trypsinogen into trypsin, resulting in autodigestion of pancreatic parenchyma  were inactivating mutations and occur in Venus-flytrap domain of CaSR-ECD. A study also revealed that CaSR expression was decreased in the case of pancreatic cancer. Which suggests that activation of CaSR has some key role in pancreatic cancer B. .In this study, we used in-silico techniques and the molecular dynamics approach to examine the influence of CASR gene SNVs on structure and function. In-silico tools and servers like SIFT, PolyPhen, CADD, PROVEAN, I-Mutant, PANTHER, PON-P2, Duet, CUPSAT, REVEL, PredictSNP, MAPP, PHD-SNP, and Consurf evolutionary analysis conferred the deleteriousness, destabilising, and pathogenicity of mutations based on sequence, structure, and evolutionary conservation analysis. The in-silico SNV impact analysis algorithm gives the result that all four mutations  were predicted to be deleterious on protein function. The Gibb’s free energy changes (DDG) upon mutation showed destabilizing effects on the protein structure. Evolutionary conservation analysis revealed that amino acid position of P163, I427, and V477 are conserved. This suggests that mutation at this location would result in structural and functional changes in the altered protein. Also, these mutations may lead to changes in physiochemical properties of overall protein and subsequently results in different structural and functional behavior.Further investigation carried out at atomic-level changes in various physical and structural characteristics of proteins/amino acids using MD simulation study of WT and mutant protein. Thus, we were able to observe a variety of behavioural changes by computing the RMSD, RMSF, Rg, SSE, H-bonds, salt-bridges, SASA, and Protein-ligand interaction. The conformational changes between the WT and mutant proteins are visulised using a time-series analysis of the RMSD over the simulation . The RMSFurther, essential dynamics analysis of concerted motion using MD simulation trajectories revealed the significant changes in dynamic behavior of protein. The major dynamic motions of protein from MD simulation trajectories were studied using principal component analysis and cross-correlation dynamics of concerted motion. This provides additional measures for studying the only motion that has a significant impact on protein function and stability. We discovered an increase in percent aggregate motion for the first two PCs after mutation, with I427S being the most affected, followed by D433H, V477A, and P163R. . When co2+ regulation, and others. Mutations in the CASR gene associated with TCP are still poorly untreated. Mutation in the CASR gene alone or combination with the SPINK1 gene is involved in the cause of pancreatitis or pancreatic cancer. CaSR mutations P163R, I427S, D433H, and V477A, are associated with TCP and reported in the mutTCPdb database. Several computational tools and methods were utilized in this study to see the impact of SNVs.The CASR gene plays a significant role in the various physiological processes of food digestion, regulation of pancreatic secretions, homeostasis role of CaThe results of this study indicate that the mutations P163R, I427S, D433H, and V477A have a significant impact on protein structure. Various measures such as deleteriousness, changes in Gibbs free energy (DDG) value, pathogenicity, and atomic-level analysis of various properties and measures using MD simulation were used. Changes in the amino acid at these specific positions, based on these observations, could harm its structural and functional properties. The evolutionary conservation analysis using the consurf server also suggests that the amino acid residues P163, I427, and V477 were buried and highly conserved. In contrast, amino acid residue D433 was exposed and slightly conserved.MD simulation, along with essential dynamics analysis, also suggests that mutation P163R has a significant impact in all MD simulation measurements when compared to other mutations I427S, D433H, and V477A. However, in the end, all mutations have a clear impact on the structural and functional aspects of the protein. These amino acid changes are thus a compelling cause of altered protein functions and behaviours. Changes in co-agonist binding behaviour at the orthosteric site and interactions with amino acid residues can also be seen in the results. This study contributes to a better understanding of the impact of SNVs on structure and function, which may lead to the development of biomarkers and therapeutics.