Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.The online version contains supplementary material available at 10.1186/s40364-022-00425-w. Biomarkers comprise biological measurements that can give an indication about a person’s medical state, disease progression or response to intervention . Thus, bIn this review we will consider how the development of fluid protein biomarker assay methods can be supported, using bioinformatics tools and data resources. While we will focus on the domain of dementia biomarker development, the recommendations given here can be used in the setting of any protein biomarker or biomarker panel.Novel biomarker development is a long and multidisciplinary process that consists of biomarker discovery, qualification, verification and clinical validation , 17.Biomarker discovery has the aim to identify novel proteins that are most suitable to differentiate between two states of interest  by means of their expression levels . There aBecause of its widespread prominence and use, we focus on MS as a biomarker discovery tool in depth hereinafter. The relative protein quantification using MS is facilitated through the ionization of the present biomolecules, followed by their separation and detection based on their mass-to-charge ratio. Importantly, preceding sample preparation usually involves the depletion of highly abundant proteins and protein digestion by a protease  Fig. 1)1). Thus,One of the major drawback of MS is the low achievable sample throughput because of the intensive sample preparation and the high associated costs of this technology . The limcapture antibody, while producing a read-out signal through the second detection antibody binding to the target [ELISA is a targeted immunodetection approach that is customarily implemented as a “sandwich” assay. These immunoassays allow the detection of protein biomarkers by capturing and immobilizing the protein target with a first e target  Fig. 1)capture ae target . The appe target . The idee target .Owing to their prominence and prevalent use, here we described an MS-to-ELISA-centered biomarker development pipeline. Note that the arguments made in the following are applicable to any workflows with the aim of establishing clinical immunoassays based on explorative biomarker studies such as mass spectrometry, proximity extension assays or aptamer-based proteomics , 21, 22.Methods for biomarker discovery and clinical validation exhibit benefits and weaknesses that make them suitable for one step but inadequate for the other . They shSeveral other difficulties and gaps arise in the pipeline of biomarker development which have been the subject of previous reviews , 19, 34 Based on the typical approach for the immunoassay development of a novel biomarker target, we wish to highlight several stages at which bioinformatics tools could enhance the process and increase the chances of successful assay design. A proposed workflow of novel immunoassay development is shown in Fig. biological context, protein origin and location, structural protein features, and proteoform complexity is vital to reveal obvious reasons to include or exclude biomarker candidates.Following the discovery of biomarker candidates by exploratory proteomics studies or other approaches such as genetic studies or biological pathway analyses , 35, 36,Even if a prioritization of biomarker candidates is not required, e.g., if only a single protein biomarker will be investigated, its characterization can still be advantageous.immunoreagent databases to consider which antibodies will fit the researcher’s requirements, e.g., regarding validation experiments, specificity and modifications. Experimental validation of the chosen antibodies at this point is recommended if no sufficient data is provided by the supplier [Available immunoreagents should be surveyed for the chosen protein targets. Commercial immunoassay kits offer the advantage of an antibody pair determined by the manufacturer and are often preferred. However, if the assay is not performing successfully or no kit is available, purchase and validation of commercial, or generation of novel antibodies is needed. It is advantageous to browse available supplier .epitope and can either be a single stretch of amino acids  or a patch of amino acids brought together closely by the fold of the protein  [In the case that a novel antibody pairing needs to be established for an assay, the choice and combination of tested antibodies is often done arbitrarily and based on a trial-and-error approach . This isepitope) .While manufacturers often disclose the immunogen, i.e., the protein or peptide fragment against which the antibody was raised, this information is not necessarily enlightening. For methods such as ELISA, that are based on the recognition of the native protein, antibodies raised against the full-length natively folded protein are preferred , 42. Thustructural protein features, proteoform complexity and protein interactions. Moreover, the epitope regions should be investigated regarding their specificity and overlap with each other. Thus, bioinformatics resources can facilitate the selection of an antibody pair with favorable epitopes that are distant from each other. Less combinations of antibodies might need to be tested to identify a suitable pairing. Additionally to computational approaches, experimental study of antibody characteristics might also be advantageous. Screening for antibody affinity can usually be added early in the selection process; an example is the use of off-rate screening [Approximating the epitopes of considered antibodies in more detail allows to investigate those areas more thoroughly. Bioinformatics tools can support to identify potential obstacles of antigen-antibody-interaction which would hinder the protein detection in the assay. Specifically investigating the epitope-containing region of the protein target, it can be helpful to examine creening .structural protein features, proteoform complexity, protein interactions, and epitopes to facilitate the production of adequate antibodies.If no commercial antibodies exist or perform acceptably, production of novel antibodies might be considered for a strong biomarker candidate; it is however a long and costly endeavor . Antibodbiological context, protein origin and location, structural protein features, proteoform complexity, protein interactions and epitope to understand the potential reasons of failure. An error analysis could reveal the unsuitability of the target protein or the chosen antibody pairing. This in turn might lead to an adapted experiment set-up that produces the desired results.If the assay development failed, an option is to revisit the list of potential biomarker candidates to make a novel selection. Beforehand, an error analysis should be performed on previously tested assays by applying any omitted bioinformatics tools regarding In the previous section various areas of interest during assay development were highlighted. Here, we wish to detail for each area which specific properties can be investigated, how bioinformatics can be utilized for these tasks at hand, and to introduce specific tools. Only freely available, easy-to-use and web-based methods and databases are considered in this review. We provide at least one state-of-the-art example while also considering reliability. Note that the pace at which new tools are released differs strongly between research fields. This is by no means an exhaustive or complete list. Where possible, more expansive literature on a certain bioinformatics topic is referenced. A summary of all mentioned resources as well as a reference to their associated steps within the workflow of novel immunoassay development Fig.  can be fAs an initial step, every potential biomarker’s associated UniProt entry should be identified. The UniProt Knowledgebase  providesProtein function can elucidate if alteration to the protein expression could be associated with the pathology of interest. Function can be well characterized by the Gene Ontology (GO) annotations. GO is a defined and consistent vocabulary that assigns to every protein associated terms of three major categories: biological processes, molecular functions, and cellular components , 91. A pKnowledge about disease associated interaction partners can also increase the confidence in a biomarker candidate. STRING is a protein-protein-interaction database collecting information from a vast number of sources such as text mining, databases, experimental evidence and computational predictions . The intIf involvement in disease can be identified, it noticeably increases the confidence in a candidate’s capacity as a biomarker. Furthermore, it might be important to establish the protein’s “specificity” as a biomarker for the intended use. One example of an “unspecific” diagnostic biomarker is neurofilament light; it reliably indicates axonal damage and is thus considered a cross-disease biomarker for axonal damage in neurological disorders . This hoEspecially for fluid biomarkers, it is worthwhile to determine a protein’s likely origin by examining its tissue-specific expression. Body fluids can contain proteins secreted from various organs and tissues; the detection in a fluid is therefore not providing much certainty yet on a protein’s origin. For instance, only approximately 20% of proteins found in CSF are brain-derived . If a CSTwo major protein expression resources are the Expression Atlas and the Human Protein Atlas (HPA). Both atlases provide trustworthy data on the tissues in which a protein has been detected. The Expression Atlas is a curated collection of gene expression results providing information on the tissues in which a protein is expressed and how it changes during disease . SimilarThe protein’s subcellular localization can show its potential as a fluid biomarker as its presence in the extracellular region might corroborate its subsequent presence in body fluids. Furthermore, location can provide additional context on the protein’s function . UniProtA more specific, but relevant, circumstance is the presence of the biomarker in an extracellular vesicle (EV) within the body fluid Fig. A. EVs arStructural protein features might be utilized to explore the protein’s suitability as an immunoassay target considering the amount of accessible surface area (ASA) that antibodies can bind to. Information on protein structure is also vital to determine the localization of epitopes or potential immunogenic peptides within the full protein. An epitope needs to lie at the surface of the protein target to allow antibody binding Fig. A. If notMolecular weight is a basic indication of protein size. A simple tool for its computation is provided on the ExPASy Server . It uses4.52 kDa . MoleculIdeally, the researcher has access to the experimentally solved protein structure to investigate its overall arrangement: a globular protein will contain many buried residues inside its core, while a more extended protein shape allows a larger portion of the residues to be potentially involved in antibody binding. The solved protein structure can also be used to map the epitope or a potential immunogen onto it to infer its location within the 3D structure and thus its accessibility for the binding antibody.Experimentally solved structures of proteins and protein complexes are collected and curated in the Protein Data Bank (PDB) , 60. EntRecently, a major advance has been made within protein structure determination due to the release of AlphaFold, a protein structure predictor that frequently achieves accuracies at the level of experimental methods . AlongsiWhile the mentioned protein structure databases  include incorporated 3D structure visualization tools, dedicated web-based structure viewers exist offering further functionalities. For instance, Mol* (“Mol star”) is the standard viewer incorporated into the PDB and is also available as a stand-alone tool . It takeTo evaluate local (secondary) protein structure and surface accessibility, informative structural properties can also be simply predicted from the protein sequence. These predictions are often less accurate but are available for any protein or peptide for which the sequence is known. Commonly predicted characteristics include ASA, secondary structure and disorder, which can offer valuable insight into the actual surface area that would be available for antibody binding in the immunoassay. A comprehensive collection of structural protein features can be most efficiently acquired from services such as PredictProtein or NetSurfP-2.0. PredictProtein is a broad prediction service with over 30 tools of different structural and functional protein features incorporated . NetSurfIntrinsically disordered regions, i.e., regions in the protein lacking a defined folding, are an important property to consider. These regions can take various configurations in solution, and do not fold into a unique structure that can be determined experimentally. Such regions have also been shown to highly overlap with the low confidence prediction regions of AlphaFold . As mostWhile it is now established that the human proteome is made up of approximately 20.000 proteins, this does not capture the full extend of the proteome diversity as splicing, protein cleavage and post-translational modifications (PTMs) create various protein variants that stem from the same gene .Researchers need to be aware of the complexity in which the biomarker candidates exist in the human body and establish which proteoforms are of interest for the specific research question.It should be established if the aim is to develop an immunoassay capable of detecting all variants of the protein target or a specific subset, e.g., one splice variant. Additionally, the knowledge available on existing proteoforms is important to facilitate optimal antibody and immunogenic peptide choice as potential obstacles, e.g., a PTM located in the epitope region, can strongly affect the binding of an antibody.About 95% of mammalian genes are affected by alternative splicing after the transcription to mRNA, resulting in multiple protein products derived from the same gene . The useUniProt provides besides the canonical reference sequence of each protein also the isoforms arising from alternative splicing.Similarly, many proteins undergo proteolytic cleavage. Often only the cleaved fragment might serve as a biomarker; examples include the AD biomarkers amyloid-rogranin , 119. IfPTMs are receiving increased attention because of their possible involvement in various diseases –122. AntProteomics studies have identified over 3000 proteins in CSF  and overThe formation of protein complexes is vital for most biological functions. If the interface of a protein interaction site is identical or overlapping with an epitope or immunogenic peptide, the antibody binding might be hindered Fig. C. Many sDatabases also provide useful information. InterPro  is a welProtein aggregation and oligomerization have high importance within the pathogenesis of several neurodegenerative diseases . The accEpitope localization and characterization may support a researcher’s epitope-specific antibody survey and selection if information from the manufacturer is insufficient. Likely epitope residues can be predicted and thus a more focused examination of the probable binding region  can be performed. As often at least a broad immunogenic region is provided for commercial antibodies, it might be helpful to compare that knowledge with the results derived from the bioinformatic prediction tools. Additionally, epitope related tools can be a convenient way to support immunogenic peptide selection as residue stretches predicted as epitopes will most likely constitute a suitable peptide for immunization as well.Figure The most widely applicable tools are sequence-based epitope predictors as only the protein sequence is required and many methods have been published with slightly differing outputs. BepiPred-2.0 is a widely cited sequence-based predictor for linear epitopes of different length . The tooThe aim of the applications mentioned hitherto is the identification of surface residues that have the potential to be recognized by antibodies. It might be difficult to determine which amino acids would ultimately comprise the epitope for the antibody of interest if many residues are predicted as epitopes . A propoOther epitope resources to be aware of are the Immune Epitope Database (IEDB)  and the The uniqueness of an epitope or potential immunogenic peptide is an important characteristic to be assessed. If an antibody can bind a structure other than the intended biomarker candidate, a false positive signal would be the result. Non-specific binding by commercially available antibodies leading to erroneous immunoassays has been reported in many scientific publications –151. OffThe challenge to search for the most advantageous commercial immunoassay kits or antibodies is a daunting task. A widely used approach of purchasing many antibodies and kits and testing them in parallel is expensive and time consuming with no guarantee for success . FrequenThe most convenient and thorough way to survey available and find trustworthy immunoreagents is the use of antibody validation databases. These collections contain more objective and reliable antibody and immunoassay evaluation data. The product range from multiple suppliers is gathered to be easily compared and the criteria for validation data are often more stringent. Several of those databases are described below and summarized in Table Antibodypedia  providesWith this multitude of information sources available a more exhaustive survey of antibodies and immunoassay kits can be performed. By letting researchers more easily identify trustworthy immunoreagents (and avoid unreliable ones) for their specific application, antibody databases can help manage the unreliability problems of research antibodies. Still, rigorous antibody validation by the researchers themselves is indispensable , and sevTo illustrate the use and interpretation of the bioinformatics tools and data resources introduced in this review, we present use cases for three proteins that are either established biomarkers or promising candidates for AD: neurogranin, tau and TREM2. These proteins were selected to cover a wide range of possible outcomes of the bioinformatics analysis. As this is a retrospective study of known or potential biomarkers, there is obvious bias; the analysis leading to the conclusion that these proteins are interesting candidates for AD is thus unsurprising. Nevertheless, we still expect that these use cases will serve as helpful examples how to interpret predictions from bioinformatics tools and annotations from data resources. The complete use cases are provided as an appendix to this review  which is comprised of its extracellular region, is comparatively less well established as an AD biomarker. It is much less specifically expressed in the brain, however, annotations show a clear connection to the brain, to CSF, and to AD. Much of the sequence of sTREM2 is part of the Ig-like domain. As this domain might be involved in binding an interaction partner and is not unique to the TREM2 protein, it does not constitute a good epitope for a TREM2-specific antibody. The C-terminal region of sTREM2 seems to be a more suitable region for antibody binding. Several predictions for TREM2 have to be considered carefully. The AlphaFold model of TREM2 highlights the limited accuracy for inter-domain positions considering the relative positions between the transmembrane helix and the extracellular domain. The aggregation prediction by Aggrescan3D 2.0 relies heavily on the hydrophobicity of surface residues. The region of highest aggregation propensity thus corresponds to the highly hydrophobic transmembrane region; however, this region is not present in the soluble form. The bioinformatics analysis of TREM2 exemplifies that it is important to consider if the predicted annotations actually fall within the matrix-specific protein product.The cross-technology translation gap between MS and antibody-based assays continues to be a major bottleneck within the biomarker development and thus a major factor limiting the successful clinical implementation of fluid biomarkers. Therefore, optimization and rationalization of biomarker assay design should receive increased attention. In this review, we examined the typical workflow of novel immunoassay development to identify steps that can benefit from the incorporation of bioinformatics tools. We determined areas of interest during the selection of appropriate biomarker candidates, antibodies and immunogenic peptides. For each area of interest we established which specific properties could be investigated with the aid of online databases, prediction and visualization tools. For each property of interest we discussed at least one specific tool and illustrated how the gained knowledge can enhance or accelerate the assay development process.Recent progress within bioinformatics has led to the release of a vast number of resources useful for fluid protein biomarker research. The value of these tools is constantly increasing as new entries are added to databases, the performance of prediction tools is improved, and the integration across different databases is promoted. Especially the release of AlphaFold and its corresponding database is expected to strongly increase the prediction accuracy of methods dependent on protein structures, e.g., epitope predictors. Note that while the AlphaFold Structure Database provides researchers with a protein structure model for every human protein, the use of these structure models in structure-based predictors should be gauged for proteins with a high disorder content. As disordered regions are defined by the absence of a definite structure, prediction on structures of highly disordered proteins are not meaningful and the use of sequence-based prediction tools is advisable for such regions.Much effort has been put into making these tools user-friendly for the general researcher. However, incomplete knowledge about which types of resources exist and which would be most suitable for the matter at hand might discourage many researchers from implementing computational methods in a meaningful way. Hence, with this review, we aim to present the scope of current available bioinformatics tools and provide explicit ideas on how to utilize them for biomarker assay development. Especially the included use cases on AD biomarker candidates offer an easy demonstration of how to use the presented tools and resources and critically evaluate the findings.While bioinformatics have the potential to save time, resources and money, the limitations of these computational resources should be contemplated, especially when basing decisions about the prioritization or exclusion of biomarker candidates and antibodies on them. The accuracy of the results can vary greatly between prediction tasks. For instance, while an independent benchmark study of sequence-based disorder predictors reported AUC scores up to 0.957 , currentIn conclusion, we expect this review to provide a valuable introduction into bioinformatics solutions for the current challenges within the biomarker assay development pipeline. The collection of suitable tools compiled and categorized here provides a starting point to incorporate the methods and can save time and resources.Additional file 1: Use cases for dementia protein biomarkers. PDF document of extended use cases for three biomarker candidates of Alzheimer's Disease: neurogranin, tau and TREM2. For each biomarker a suitability survey was performed and then compared to the current knowledge of these proteins. The document contains figures and results of the used bioinformatics tools and data resources as well as our interpretation of these results.


The technological revolution to measure global gene expression at the single-cell level is currently transforming our knowledge of the brain and neurological diseases, leading from a basic understanding of genetic regulators and risk factors to one of more complex gene interactions and biological pathways. Looking ahead, our next challenge will be the reliable measurement and understanding of proteins. We describe in this review how to apply new, powerful methods of protein labeling, tracking, and detection. Recent developments of these methods now enable researchers to uncover protein mechanisms in vivo that may previously have only been hypothesized. These methods are also useful for discovering new biology because how proteins regulate systemic interactions is not well understood in most cases, such as how they travel through the bloodstream to distal targets or cross the blood–brain barrier. Genetic sequencing of DNA and RNA have enabled many great discoveries in the past 20 years, and now, the protein methods described here are creating a more complete picture of how cells to whole organisms function. It is likely that these developments will generate another transformation in biomedical research and our understanding of the brain and will ultimately allow for patient-specific medicine on a protein level. Proteins play the most active roles in cellular function, yet technologies to study molecules at the DNA and RNA level have developed at a much faster pace compared to current methods available for studying proteins . This liProtein tracking is useful for understanding how proteins secreted into the bloodstream can affect other tissues exposed to them through the vasculature . The braAnother example of the need for protein tracking is intercellular communication between the same or different cell types within a single tissue. Proteins mediate intricate signaling pathways between cells to coordinate orchestrated responses, such as with immune cell infiltration or responses to bacterial/viral infection . The BBBRecently, more tools have been developed to label and track specific proteins or proteomes for disease and BBB model systems. These methods are still under development and often require overcoming technical hurdles to be used. Therefore, we will describe here how these tools can be practically used to explore important aspects of proteins in biology, offering an overview of capabilities with current and upcoming technologies. We will mainly discuss the use of these methods in the context of BBB transport as a particularly interesting barrier to protein movement, but these tools may be applied in many other biological contexts, often even more easily. Some of these tools may be used in humans either now or in the future to diagnose disease or directly understand human biology, but perturbing entire proteomes may have unforeseen consequences that necessitate tools for non-human model systems. Ideally, tools used in humans should be transient and reversible, such as non-covalent binders of proteins that are rapidly cleared from the body in hours to days. Proteins can also be sampled with or without labeling from interstitial fluid microdialysis or directly from other fluids . In-human protein labeling and tracking tools include isotopes, binders, nanoparticles, and tracers that associate with or mimic proteins of interest, but these options almost always do not permanently change proteins or proteomes as some model system tools do. We will first describe useful methods of protein labeling, then elaborate on how labeled and unlabeled proteins can be tracked and detected in model systems. We cite specific examples as references to refer to that should allow researchers to quickly adapt a tool of interest to their research with both methods and details. These methods are useful for studying protein function in biological contexts, a critical part of the ultimate goal in understanding the complexity of model organisms and human health that is still elusive to understanding.Protein labeling has a long history with some tools still remaining relevant today. Evans blue (EB) described 100 years ago in 1920 is an example of a dye that is still widely used, in fact even more frequently over the past decade, as it is an inexpensive and clear way to visualize the permeability of organs, critical to understanding the BBB , 16. EB 3+ –6058–60]As discussed above, there are many methods to label proteins Fig. . These lFurthermore, GFP and similar genetically encoded fluorophores have long been a useful tool for tracking in several contexts. When inserted into the locus of growth hormone in transgenic mice, GFP can be used to visualize secretion in the pituitary . GFP canAn alternative to the Cre-loxP system is the use of viruses harboring tissue or cell type-specific promoters to deliver gene constructs, like a bioorthogonal aaRS or tagged protein, into specific cell types. Lentivirus and adeno-associated virus (AAV) are the most commonly used viruses for gene delivery into cultured cells or in vivo. Lentivirus is a retrovirus that integrates its genomic information into the host’s genome which is advantageous for making stable cell lines in vitro but can cause insertional mutations in vivo . AAVs, oL274G under the control of the CaMK2a promoter [L274G parabiosed with an unlabeled mouse was used to show infiltration of labeled blood protein factors into the muscle of the unlabeled mouse [Y43G or MmPheT413G can show the impact of cancer-related proteins on distal tissues, an example of how protein tracking can determine the impact of disease on indirectly affected tissues [Labeling of proteins using ncAAs has been recently used to track even entire proteomes in vivo. For example, the proteome of excitatory neurons was specifically labeled in mice using MmMetRSpromoter . These mpromoter . A separpromoter . In anoted mouse . This st tissues .One advantage of ncAAs over genetically encoded fluorophores like GFP, is the ability to use more diverse imaging techniques to track proteins. GFP labeled proteins have long been imaged in live cells, a simple and reliable method of tracking , 73. SupIn addition to the above imaging methods, proteins can be detected using histology methods. Proteins have been traditionally imaged using histology by either antibodies against the protein of interest or a tag attached to the protein of interest. Click chemistry with ncAAs is a new, important way to detect proteins using histology. Like protein tags and chemical linked labels, ncAAs can be used to detect proteins where there is no effective antibody for a certain protein because a fluorophore can be attached to any targeted protein that has the required ncAAs incorporated . In addiMass spectrometry is becoming an increasingly more powerful method to detect proteins and proteomes. SILAC for differential detection of proteins by mass spectrometry is one such application that has been widely used to make biological discoveries . However64Cu, 68 Ga, and 177Lu. Tissues can then be analyzed ex vivo to determine biodistribution of proteins or even cells. This technique has successfully been used to detect the transport of target proteins and plasma into the brain or spinal cord after crossing the BBB [Autoradiography is another useful method for protein detection. In this technique, proteins can be labeled with radioisotopes and injected into animals. One common method is to chemically conjugate the desired proteins to the chemical chelator DOTA which can further react with radionuclides including  the BBB , 65.In line with mass spectrometry proteomics that can detect thousands of proteins, antibody and aptamer arrays have been also used for multiprotein detection. The main advantage over mass spectrometry here is that arrays can have high sensitivity to proteins even at low concentrations and have a lower technical barrier to usage. However, arrays have fallen behind in many respects to mass spectrometry because arrays are only as good as the antibodies or aptamers they contain. If many antibodies/aptamers are not specific to the desired protein, false positives or false negatives can often appear. New protein detection platforms have addressed these issues by developing probes with very high sensitivity and specificity for protein targets, such as platforms multiplexing thousands of proteins by using pairs of antibodies combined with rolling circle amplification PCR employed by OLINK or aptamer arrays developed by Somalogic , 86. DatNew methods are upcoming that will advance protein detection even further. Notably, highly parallel identification of single protein molecules offers the promise of RNAseq level sensitivity for detection of proteins . This meRNAseq has been a major force driving our understanding of homeostasis, aging, and disease states of cells and tissues. However, protein changes often do not correlate well with RNAseq and require the use of protein tracking and detection methods for understanding of the biology underlying these processes in organisms. Tools to measure and detect proteins have developed significantly over the past decade, but they have not yet reached their prime to understand the full span of the proteome. Newer tools for protein detection described here offer temporal and cell-specific proteome coverage across a large range of up to thousands of proteins. Newer protein labeling methods offer more precise control over the proteome, even in vivo in mice. Mass spectrometry developments have enabled these technologies even more by increasing proteome coverage. By applying these tools to model systems, new mechanisms of mammalian biology that have implications in the detection and treatment of human disease will be uncovered. These tools will also likely be applied directly to humans where effects are transient or produce no discernable negative effects in model systems. We predict that now as protein labeling and detection methods are becoming accessible to researchers, our understanding of the proteome, including the plasma proteome, will once again transform our insight into the biology of living organisms and diseases in the coming years.


Pro), and spike protein. The molecular docking, prime MM-GBSA, induced-fit docking (IFD), and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins. The ligand-binding stability for the proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro), and 6M1D (ACE2), was in the order of nintedanib > quercetin, nintedanib > darunavir, nintedanib > baricitinib, respectively. The MM-GBSA, IFD, and MD simulation studies imply that the drug nintedanib has the highest binding stability among the shortlisted. Nintedanib, primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing for us against COVID-19.The coronavirus disease (COVID-19) is a pandemic that started in the City of Wuhan, Hubei Province, China, caused by the spread of coronavirus (SARS-CoV-2). Drug discovery teams around the globe are in a race to develop a medicine for its management. It takes time for a novel molecule to enter the market, and the ideal way is to exploit the already approved drugs and repurpose them therapeutically. We have attempted to screen selected molecules with an affinity towards multiple protein targets in COVID-19 using the Schrödinger suit for in silico predictions. The proteins selected were angiotensin-converting enzyme-2 (ACE2), main protease (M The novel coronavirus disease COVID-19) was first identified in Wuhan city of China. The first case was reported in December 2019. A woman aged 54, diagnosed with severe acute respiratory distress syndrome with asthenia, fever, and other respiratory difficulties, such as a low respiratory rate and a decreased oxygen saturation in the body, was found to be infected with the virus. This virus was first named the ‘novel coronavirus’ (nCoV); later, the nomenclature was changed to ‘severe acute respiratory syndrome corona virus-2’ (SARS-CoV-2). The World Health Organization declared this pandemic disease due to SARS-CoV-2 as COVID-19 9 was fir. SARS-Copro, papain-like protease (PLpro), nsp13 , nsp12 , nsp14 , nsp15 , nsp16 , and nsp10, while the structural proteins include S-protein, envelope small membrane protein (E), membrane protein (M), and nucleocapsid protein (N) [SARS-CoV-2 proteins are of two classes, structural and non-structural proteins (nsp). The non-structural proteins include Mtein (N) . Favipirtein (N) . Both retein (N) . There ipro consistently, while phenoxymethylpenicillin and carbenicillin had high potential to act as anti-COVID-19 agents, which can be used alongside antiviral agents [pro, thereby acting as anti-COVID-19 agents [pro as proteins for all of the studies.Computational modeling helps to identify a potentially useful molecule from a large library of molecules in less time by using various tools, such as molecular docking, Prime molecular mechanics generalized Born surface area (Prime MM-GBSA), induced-fit docking (IFD), followed by molecular dynamic (MD) simulation for the selected molecules . In our l agents . In anot9 agents . We have9 agents . In anotThe protein crystal structures were downloaded from the proteins data bank (PDB) and UniProt, while the structures of the ligands were taken from PubChem. The Maestro Molecular modeling platform  was used to perform the molecular docking studies, followed by Induced-Fit Docking, Prime MM-GBSA, and MD simulations using Schrodinger software. Molecular docking was used to study the molecular interactions and binding affinity between the selected molecules and the target proteins, which indicates the behavior of the ligands (drugs/molecules) at the target protein site and elucidates the underlying molecular mechanism. Then, Prime MM-GBSA calculations were conducted, followed by Induced-Fit Docking. The top-hit molecules were then subjected to MD simulation to evaluate the binding stability of the ligand on the target protein.pro (PDB ID: 5R82), and spike protein (PDB ID: 1ZV8). The crystal structure of each protein was selected based on its resolution from PDB (http://www.rcsb.org/ accessed on 11 July 2022).Based on the literature, there are three important protein families that are involved in the pathogenesis of COVID-19, including ACE-2 (PDB ID: 6M1D), MIn this study, antiviral, anti-inflammatory, and immunomodulatory molecules were selected from the literature based on their mechanism of action, as provided in The protein crystal structure was processed using Epik Protein Preparation Wizard , where missing hydrogens, amino acid residues, and side chains were added. A proper ionization state for the protein residues was generated, the water molecules were removed, and the H-bond network was generated. The protein structure was energy minimized using the OPLS3 force field . The SiteMap tool was used to identify the druggable ligand-binding pocket in the proteins that lack a bound ligand in their crystal structure to identify the potential binding site for the ligand. The SiteMap module returns potential sites on the protein, which are ranked based on their SiteScore and DScore. Based on these values, a binding site was selected, and the ligands were docked on the proteins .The Glide module was used to perform molecular docking. The receptor grid was generated using the receptor-grid-generation tool, which defines the region on the protein onto which the module needs to introduce the ligands for docking. The receptor grid file was loaded, and the prepared ligands were selected from the table. The docking was performed in the extra precision (XP) mode. The docked ligand poses were then analyzed for interaction patterns with the protein; the ligand pose with the best interaction with the protein was selected. The top hits obtained from molecular docking were selected and subjected to induce-fit docking, prime MM-GBSA, and MD simulations .MM-GBSA was used to determine the ligand binding energies and ligand strain energies for the top hits in the docking studies. The prime MM-GBSA module of Schrödinger was employed for this purpose. The OPLS3e force field was used with the VSGB solvent model, while the ligands and receptors were taken from the project table and workspace. As the MM-GBSA binding energies are the approximate free energies of binding, a more negative value indicates a stronger binding .The induced-fit docking (IFD) protocol was carried out using the induced-fit tool in Maestro  on the selected proteins with the top hits obtained from the docking studies and prime MM-GBSA. The prime reason for performing IFD is to permit flexibility to both ligands and proteins, which is restricted in docking studies. IFD is reported to be robust and accurate in predicting the binding affinity between the ligand and the protein pocket of the protein. The ligand, docking, and protein refinement were carried out using glide and prime, respectively, in the IFD tool.An MD simulation was performed for the shortlisted molecules from the results of IFD and MM-GBSA using the Desmond module . Initially, the solvated complex system was prepared using TIP3P as a predefined solvent model, and the iso-osmotic condition was maintained in this stage. The System Builder module was used for the preparation of the above system. The solvated system was minimized using the minimization module. It was then loaded and minimized using the 2000 maximum iterations and 1.0  convergence threshold. The minimized solvated system was used to run the MD simulation. The MD simulation was performed with the NPT ensemble for 100 ns at 1.01 bar pressure and a 300K temperature by using the Nose–Hoover chain (1 ns as relaxation time) and Martyna–Tobias–Klier (2.0 ns as relaxation time) as the thermostat and barostat method, respectively. The MD simulation results for all of the ligands were analyzed by generating the simulation interaction diagram . The MD pro, PDB ID: 5R82) with a known binding site, whereas SiteMap was performed for the proteins in the cases where their crystal structure lacked a bound ligand, due to which the binding site is not clearly known . SiteMap helps to identify the druggable binding site on the protein based on analysis of the entire protein. The results of the SiteMap analysis were obtained as Site-score and D-score, as depicted in Molecular docking was performed on the protein main protease , ligand RMSD, protein root mean square fluctuation (RMSF), ligand RMSF, and the interactions observed between the ligand and the amino acids of the protein, among other results. Of all the MD simulation runs, only nintedanib on 6M1D showed a slight deviation between run 1 and run 2, while every other MD simulation run yielded the exact same result. In addition to RMSDs, it is also important to know the intermolecular interactions between the ligand and the protein, such as H-bonds, hydrophobic interactions, π-π stacking, π-cation, salt bridges, and water bridges. These can be graphically visualized by protein–ligand contact plots through a bar diagram and are reported as the fraction interaction (in %). Additionally, the P-RMSF plot from the simulation interaction diagram (SID) is illustrated in Similar to protein RMSF, pro protein(5R82), nintedanib, had the best docking score and ΔG value, but darunavir had a greater IFD score, fluvoxamine and favipiravir when compared, had decreased docking scores and ΔG than that for nintedanib and darunavir. However, based on the amino acid interactions, it was evident that darunavir has more interactions than nintedanib, while fluvoxamine and favipiravir showed fewer interactions. This was the reason why nintedanib and darunavir were selected for MD simulation, as they outperformed the other ligands. For spike protein (1ZV8), quercetin exhibited the best docking score and IFD score, while nintedanib had a greater ΔG value. Lopinavir and hydroxychloroquine had poorer docking scores among the selected ligands, but lopinavir had an IFD score that was greater than nintedanib and a ΔG value greater than quercetin. Upon inspecting the amino acid interactions, it was clear that nintedanib showed more interactions, followed by quercetin, while lopinavir and hydroxychloroquine had fewer interactions. Hence, quercetin and nintedanib were selected for MD simulation. A considerable number of computational docking and predictions for the treatment of COVID-19 are in progress. The success of repurposed drugs such as remdesivir, favipiravir, and dexamethasone is the stimulus for further computational predictions ,49. A stpro protein, from the RMSD plot , nintedanib and quercetin show an RMSD value within 2.5 Ȧ, and both ligands do not occupy the protein-binding pocket for the entire duration of the simulation  and ∆G (−50.42), but the IFD score (−643.67) was less when compared with darunavir. For ACE-2, nintedanib showed the highest docking score (−6.056) and ∆G value (−44.53). In the MD simulation of nintedanib, after 40 ns of simulation separated away from the Mpro, respectively, protein-binding pocket, whereas it was stable with the spike protein and ACE-2. Currently, COVID-19 management options are supportive therapy and preventive control measures in hospital settings. It takes a great deal of time for USFDA to receive and approve drugs specific to SARS-CoV-2, due to which researchers resort to repurposing existing approved molecules for the management of COVID-19. Drugs such as favipiravir, nelfinavir, ivermectin, lopinavir, remdesivir, baricitinib, and tocilizumab are being tested clinically to evaluate their effectiveness against SARS-CoV-2 . Though Nintedanib is used in the treatment of idiopathic pulmonary fibrosis (IPF), systemic sclerosis-associated interstitial lung disease, and non-small cell lung cancer along with docetaxel . Nintedapro, and ACE2 proteins. Thus, nintedanib could be a suitable molecule for the treatment of COVID-19, but in vitro and in vivo experimental results should substantiate these findings.Based on the results of the molecular docking score, ΔG value, and the IFD score, the best molecules, nintedanib, quercetin, darunavir, and baricitinib, were shortlisted. The most preferred interaction was seen in nintedanib in the MD simulation with spike proteins, M


Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the  The mechanistic  target of rapamycin (mTOR) is a serine-threonine protein kinase, located on chromosome 1p36.2. Extensive research has found that this 289-kDa protein regulates several fundamental cellular processes. These processes can be as varied as protein synthesis and autophagy [C-terminus of the protein contains the PI3-K kinase domain whereas its N-terminal contains at least 20 HEAT (huntingtin elongation factor-3-protein phosphatase-2A TOR1) repeats, which mediate their interaction with associated proteins [The kinase belongs to the PI3K-related kinase (PIKK) family and is the catalytic component of two major multi-protein complexes mTORC1 and mTORC2 which activate two downstream signaling pathways. Both complexes share mLST8 and Deptor in addition to the catalytic subunit. mTORC1 also comprises Raptor, and PRAS40 . The C-tproteins .mTOR has been found to be abnormally over-activated in more than 70% of cancers . The abemTOR gene were linked with an increased risk of esophageal squamous cell carcinoma [SNPs are the most common genetic variants that are present in the human genome. They refer to a variation of a single nucleotide at a specific genomic position among individuals and occur approximately every 100–300 base pairs . This tyarcinoma , 12. Sucarcinoma .in silico analysis via available web-based bioinformatics tools, it is feasible and also cost-effective to study a large number of SNPs present within a particular gene. Computational genomics has made remarkable contributions to the field of disease-associated nsSNP analysis. Several algorithms have been developed for the accurate prediction of the phenotypic effects of the nsSNPs [Missense non-synonymous SNPs (nsSNPs) have always been regarded with much concern. They lead to an amino acid change within the coding sequence and therefore can affect the protein’s activity. Determining the effect of multiple nsSNPs experimentally can be expensive and laborious. However, by using e nsSNPs . The stre nsSNPs –17. Signe nsSNPs , kinesine nsSNPs , cardiome nsSNPs , etc.mTOR gene have been collected, screened, and analyzed to find out the most potentially harmful nsSNPs. The primary focus was given on how these genetic variations affected the protein’s structure, stability, and function as well as the physicochemical properties of the respective amino acid residues. Besides, this study also included an analysis of the non-coding SNPs that are located at the mTOR gene to identify any potential harmful effect of such SNPs in this locus.The results from this study are intended to have worldwide reproducibility, require less computational time and processing memory and ensure ubiquitous dissemination. Although there are many standalone software available, they require expertise in programming languages. They are also machine-dependent. Therefore, to ensure reproducibility and repeatability, web servers were preferred to standalone computational software in this study. Here all the nsSNPs within the mTOR gene.Altogether 11 nsSNPs were selected as most deleterious in this study. Three of the polymorphisms  were located within interacting regions of the protein, and four of the polymorphisms  were located within a core domain indicating they might affect the protein’s native structure. All of the nsSNPs were found to decrease the protein’s stability. Three of them  were found to be associated with several different carcinomas and detailed structural analyses revealed that these polymorphisms might alter C alpha distances, bond lengths, and bond angles and thus might disrupt the protein’s structure and function. In addition, one non-coding SNP (rs12139042) was found to affect miRNA binding sites which suggests that it could interfere with miRNA regulation of the mTOR protein. Overall, this analysis will provide the groundwork for further investigation into the structural and functional impact of the SNPs in the mTOR protein sequence was obtained from the UniProt database [https://www.ncbi.nlm.nih.gov/snp/) [https://www.ncbi.nlm.nih.gov/clinvar/) [https://www.disgenet.org/) [https://asia.ensembl.org/index.html) for the respective protein ID [The human database  (UniProtov/snp/) , ClinVarlinvar/)  and DisGet.org/) . dbSNP cotein ID .in silico prediction tools were utilized. A large number of computational tools are available at present. Since no single bioinformatics tool has been demonstrated to be most effective in analyzing the harmful effects of nsSNPs, multiple tools were opted for, based on the assumption that combining scores from several tools will greatly strengthen the accuracy and reliability of the predictions. In this study two different approaches were taken into consideration while choosing the tools.To determine whether the nsSNPs will confer any deleterious effect upon mTOR protein’s 3D structure and function, in total, 10 Firstly, the prediction tools were selected in a way so that they covered four different methods including sequence homology-based method, supervised learning method, protein sequence and structure-based method, and consensus-based method. The following tools were included in the study: SIFT (Sorting Intolerant from Tolerant) , 27, PROIn another approach, two different groups of tools were included in this study. The first group of tools was used to predict the impact of the nsSNPs on the protein function and they included SIFT, PROVEAN, Mutation Assessor, CADD , PolyPheTools from the two different approaches have been overlapping in some cases. Since combining all these tools cover a wide range of prediction approaches, they may provide a high accuracy level in prediction. A short description along with the input parameters and accuracy rate of all the tools are presented in https://consurf.tau.ac.il/) [The degree to which an amino acid position is evolutionarily conserved, reflects its structural and functional importance. Therefore, the conservancy of each position in the amino acid sequence of the mTOR protein was analyzed by the ConSurf server (.ac.il/) . This tohttps://services.healthtech.dtu.dk/service.php?NetSurfP-2.0) was used [To determine the surface accessibility of each amino acid residue of mTOR protein, NetSurfP-2.0 (was used . This wehttps://www.musite.net/) was applied to predict potential PTM sites and corresponding probable modifications in the mTOR protein sequence [Post-translational modifications within a protein molecule are also crucial. They introduce new functionalities and exert control over protein structure and function by modulating intra- and intermolecular interactions. In this study, the web-based tool MusiteDeep (sequence . Rather http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) and MUpro (http://mupro.proteomics.ics.uci.edu/). I-mutant 3.0 predicts the effects based on a support vector machine [Whether the amino acid substitutions affected mTOR protein’s stability, two different web tools were applied: I-mutant 3.0 ( machine . MUpro i machine . In I-Mu machine .https://www3.cmbi.umcn.nl/hope/) [http://mutpred.mutdb.org/) was also utilized for this purpose. MutPred2 predicts the phenotypic impact of the amino acid alterations based on a neural-network method [To analyze the structural and functional effects of the point mutations, the HOPE (Have (y)Our Protein Explained) (l/hope/)  server wk method . The imphttp://www.mutation3d.org/about.shtml) was applied to find out whether the amino acid substitutions belonged to any cluster that could reveal any functional hotspots of mutations [Mutation3D (utations . Based ohttps://string-db.org/) database was utilized to find out with which proteins mTOR interacts [The knowledge of the interaction partners for a protein is crucial to understanding its function, structure, molecular action, and regulation. the STRING (nteracts . The prohttp://cscape.biocompute.org.uk/), CBioportal , and canSAR Black (https://cansarblack.icr.ac.uk/). CScape depends on a statistical approach to identify cancer-driving mutations with a 91% balanced accuracy in coding regions of the genome [mTOR gene was queried against the curated set of non-redundant studies including 190 studies and 59519 samples. The resulting missense mutations that were found in patient samples with different types of cancer were retrieved from the analysis. Another resource that was used for retrieving known associations between the SNPs with cancer studies was canSAR Black. It is a multidisciplinary knowledge base for translational research on cancer [Several different tools were applied to determine the oncogenic potential of the selected somatic point mutations including CScape (e genome . In the e genome . In thisn cancer . In thishttps://swissmodel.expasy.org/) [https://saves.mbi.ucla.edu/) [The 3D structures of the wild-type protein and proteins with the mutated amino acids were modelled in the homology modelling server- SWISS-MODEL (sy.org/) . In thissy.org/) , and HHbsy.org/) . The quala.edu/) .After all the mutated models as well as the wild-type protein model were predicted and verified, they were analyzed in TM-align  and PyMOThe non-coding SNPs located at the 5´ and 3´ UTRs were filtered out from the Ensemble database with a global minor allelic frequency (MAF) value between 0.05 and 0.5 . Later tThe entire study design including the various tools used is shown in mTOR gene including 57529 variations in the intronic region and 3446 variations in the coding sequence. Out of the SNPs in the coding sequence, 1903 SNPs were classified as non-synonymous SNPs and were retrieved from the database. Another 408 variations were found documented in ClinVar [in-silico analysis.The NCBI dbSNP database  containe ClinVar  and 26 i ClinVar . The oneUsing SIFT algorithm , 27 the On the other hand, PolyPhen-2  predicteOf these 38 nsSNPS, 36 were found to be deleterious by PROVEAN  score asWhen these nsSNPs were analyzed by Mutation Assessor , it was The deleteriousness of the single nucleotide variants was also cross-checked by PhD-SNP  and PMutWhen the chromosomal coordinates of these nsSNPs were analyzed by CADD (Combined Annotation-Dependent Depletion) , the scaConSurf estimates the degree of conservation of the amino-acid residues among their close sequence homologs and thus identifies the functional regions of a protein. For this analysis a neighbor-joining phylogenetic tree was constructed using multiple sequence alignment (MSA) data that was built by the default program, MAFFT (Multiple Alignment using Fast Fourier Transform). Also, a position-specific conservation score on a scale of 1–9, where 9 indicates the most conserved region and 1 indicates the most variable region was provided for each residue . It was NetSurfP-2.0 analysis revealed the relative surface accessibility of each amino acid residue of mTOR protein by assigning a percentage score to them. If the score was more than 25%, it implied that the amino acid residue was exposed. In this work, D944V, R1161G, K1452N, E1610K, R1616C, and N2043S positions were all predicted to be in an exposed position . InteresWhen the nsSNPs were analyzed by I-Mutant 3.0 and MUpro servers, it was found that all of them decreased mTOR protein’s stability as represented by their score which was less than 0 for every mutation. The free energy change (ΔΔG) values along with their signs predicted by the two servers are presented in Afterwards, the nsSNPs were run through project HOPE which aims at providing insight into the structural effects of a mutation including amino acid size, charge, hydrophobicity, spatial structure, and function.The detailed analysis for each of the substitutions is presented in In addition, some of the amino acid positions including Y1151C, A1513D, and T1977R are within the core domain of the protein. Changes at these residues were predicted to affect the core structure of the protein. Particularly with Y1151C, it was predicted that such a substitution would create an empty space within the core. In this case, the mutant residue became smaller than the wild-type residue and that might be too small to make multimer contacts. Moreover, conservancy analysis showed that at 619, 944, and 1610 positions, although the wild-type residues were not conserved, the mutant ones were also not commonly observed in the homologous sequences. Therefore, this analysis also predicted these mutations to be probably damaging to the protein.Certain effects of two amino acid positions were mentioned in the HOPE report. R1161G mutation introduced a glycine at this position and glycines are very flexible. This mutation was therefore predicted to disturb the required rigidity of the protein at this position. Another was N2043S substitution, which is located in a region that interacts with the FKBP1A/rapamycin complex. This amino acid change could disturb this region and interfere with this interaction.When the mutations were next analyzed by the MutPred2 server, 9 out of 11 nsSNPs scored higher than 0.8 which indicated that they had high pathogenic properties. In this server, it was predicted whether these mutations caused any gain of intrinsic disorder. Additionally, whether they caused structural modifications and DNA binding alterations were also predicted with P-value and Probability value. It was found that two of the mutations A1513D and T1977R caused loss of ubiquitination, while R619C caused loss of acetylation. In addition, several of the alterations including K1452N, A1513D, R1616C, and T1977R led to altered transmembrane properties. Similarly, E1610K and R1616C variations caused an altered disordered interface. The details of the MutPred2 results are given in the Lastly in this section, mTOR protein was also analyzed by mutation 3D web server. All 36 nsSNPs were given as input and it was found that amino acid residues at positions 1609, 1610, and 1616 belonged to a cluster with a P-value of 6.25e-02.From the STRING network, the 10 major interacting molecules with mTOR protein were revealed which included Eukaryotic translation initiation factor 4E-binding protein 1, GTP-binding protein Rheb, Regulatory-associated protein of mTOR (RPTOR), DEP domain-containing mTOR-interacting protein (DEPTOR), Rapamycin-insensitive companion of mTOR (RICTOR), Target of rapamycin complex 2 subunit MAPKAP1, Serine/threonine-protein kinase ULK1, Ras-related gtp-binding protein c/d (RRAGC), Ribosomal protein S6 kinase beta-1 and Tuberin, a tumor suppressor protein . Since sThe oncogenic potential of the point mutations was first assessed by CScape. The results showed that L509Q, D944V, A1513D, E1610K, and T1977R were deleterious, predicted with the highest confidence score. Next, these mutations were also analysed by the CBioPortal server over 190 non-redundant curated sets of studies. The position of each of the missense or non-synonymous SNPs was checked and it was found that several of these mutations were present in different cancer patients’ samples. The detailed results are shown in It could be seen that in some cases the results from CBioPortal and CanSAR Black overlapped with each other while in other cases novel associations were revealed. Among the cancer types, uterine endometrial, melanoma, renal clear cell carcinoma, kidney, prostate, and lung cancer, each was found to be linked with two different mutations. Interestingly, L509Q and D944V substitutions were not found to be associated with any cancer study although Cscape predicted these mutations to be oncogenic with high confidence.The 3D structure of the mTOR protein was predicted in the SWISS-Model workspace using 6zwm as a template . Also, tStructural comparisons were needed to be performed to find out whether the mutated protein structures differed from the wild-type protein structure. For this purpose, two different scores were analyzed, and the results are also shown in Based on this observation, the predicted 3D structures of the mTOR protein containing the following mutations: R619C, A1513D, and T1977R were visualized in PyMOL and Discovery studio. Particular focus was given to how these mutated residues differed from the wild-type version in their interaction with their neighboring residues in terms of bond angle, C alpha distance, and the number of hydrogen (H) bonds. When the angle between 619R with adjacent residues at 618 and 620 were measured, it was found to be 93.5 Å with respect to O atoms, and when the wild-type residue was replaced by Cysteine, the angle changed to 97.2 Å. When alanine was mutated to aspartic acid at position 1513, the angle changed from 90.6 Å to 90.5 Å. And when threonine changed to arginine at the 1977 position, the angle decreased from 81 Å to 78.3 Å. In all three cases, the structures of the mutated proteins were markedly changed at the site of the point mutation. Similarly, when the distance between the C alphas of the tripeptides containing the altered amino acid in the middle, was measured, all three showed changed distances. In the case of R619C mutation, the C alpha distance between 618 and 619 positions increased from 3.0 Å to 3.1 Å while the distance between 619 and 620 residues decreased from 3.2 Å to 3.1 Å. In the case of A1513D substitution, the distance increased between 1512 and 1513 positions from 3.1 Å to 3.2 Å. Again, for T1977R, the distance increased between 1977 and 1978 residues from 3.1 Å to 3.2 Å. When the C alpha distances changed due to these alterations, the dihedral angles also got affected which would ultimately affect the shape of the alpha-helices.Using UCSF Chimera, the hydrogen bond interactions of these three residues were also visualized. In every case, the number of H bond interactions remains unchanged although the distance changed. The detailed result is shown in The predicted structures of all the mutant proteins were also analyzed in Missense 3D and interestingly for the same three amino acid variations, structural damage was predicted. For R619C polymorphism, it was predicted that since a buried charged residue was getting replaced with an uncharged residue, it may disrupt a wild-type salt bridge. The salt bridge was between the NE atom of ARG 619 and the OD1 atom of ASP 666. For A1513D substitution, a protein folding problem was predicted as a buried hydrophobic residue was getting replaced with a hydrophilic residue and thus a charge was introduced into a buried residue. Finally, for T1977R substitution, a change was predicted between the buried and exposed state of the target variant residue where the wild-type threonine was buried and mutant arginine was exposed.Only three non-coding SNPs could be retrieved from the Ensemble database with a global minor allelic frequency (MAF) value ranging between 0.05 and 0.5. In RegulomeDB database analysis, one (rs2295079) out of the three SNPs scored 0.88219 and ranked 2b which indicated that several datatypes including TF binding, any motif, DNase Footprint, and DNase peak were available for the chromosomal position. The probability score that was close to 1 also suggested that the respective SNP would most likely be a regulatory variant. Afterwards, the three SNPs were checked in PolymiRTS Database mTOR gene to be associated with acute leukemia, colon cancer, gastric cancer, and many other types of cancer [mTOR locus and their potential effects on the protein’s structure and function have not yet been characterized. In the present study, a comprehensive in silico analysis has been performed using computational tools to identify potentially deleterious coding and non-coding SNPs within the target gene.As a major regulator of cell growth and proliferation, the mTOR signaling pathway influences several different key processes in the body including apoptosis, growth, and autophagy of cells . The prof cancer  as well f cancer . HoweverSeveral different tools were combined for the initial screening of the most deleterious nsSNPs to ensure a robust and accurate prediction with increased reliability. Some tools including SIFT, Mutation Assessor, and PROVEAN based their prediction on parameters like sequence homology, evolutionary conservation, and physical properties of the amino acids. Others like SNAP2 and PolyPhen2 relied on machine learning methods to predict the structural and functional impact of the variations. Some additional tools including PhD-SNP, SuSPect, and PMut were also included in the analysis to directly assess whether the polymorphisms were associated with pathogenicity. In total, 11 nsSNPs were selected as most deleterious as they were predicted to be high-risk by all the SNP prediction algorithms applied in this study.The conservancy analysis revealed six of the selected SNPs  were located at functionally and structurally important regions of the protein as their conservation scores were high. Likewise, from the UniProt database, it was found that three of the polymorphisms were located within regions that were vital for interacting with other proteins . N2043S All of the nsSNPs were found to decrease the mTOR protein’s stability as revealed by the negative free energy change values calculated in I-Mutant 3.0 and MUpro servers. Seven variations including L509Q, R619C, Y1151C, R1161G, A1513D, R1616C, and N2043S were shown to be largely destabilizing suggesting they might affect the protein’s folded structure. The structural effect of the selected variations was also analyzed by running HOPE projects. It was observed that eight of the 11 nsSNPs went through either a change in their charge or loss of their charge due to the alterations and that could result in interrupted interaction with other molecules. In addition, it could be seen that nine of these high-risk amino acid variations impacted either hydrogen bonds or salt bridges, or both within the mTOR protein. To further strengthen the predictions in this study, the MutPred2 tool was also applied which scores the probable pathogenicity of the amino acid variations showing the affected molecular mechanisms behind their pathogenicity. It was found that three of the mutations resulted in altered PTM sites, another four of the mutations resulted in the loss of transmembrane property and another one affected the secondary structure. Overall it could be seen that the deleterious nsSNPs could potentially affect the mTOR protein’s native structure and thereby function.For a more detailed structural insight into the effect of the polymorphisms, the mutated models of the protein for the corresponding amino acid substitutions were generated in the SWISS-Model workspace. The RMSD and TM align values were calculated to determine the extent of change in the 3D protein structures due to the mutations. The RMSD value was quite low (less than or around 0.1) for all the mutated models suggesting that the mutant protein structures did not deviate much from the wild-type protein structure. However, for six of the amino acid substitutions, the TM align values came between 0.5 and 1 which indicated dissimilarity between the wild-type and mutant structures. Afterwards, three of these mutant structures including R619C, A1513D, and T1977R were further analyzed as their RMSD values were also comparatively higher than the other variant structures. Through structural analysis it was found that although the number of hydrogen bonds did not change between wild type and mutated residues, the bond angles, C alpha distance as well as the length of the H bonds changed due to the amino acid alterations further confirming that the polymorphisms may disrupt the protein’s native structure and stability. Several studies have shown that the interplay between protein backbone geometry and local conformation has an important contribution to protein stability . Since bAmong the non-coding SNPs, rs2295079 was predicted to have a regulatory role over mTOR protein as it had a prediction of transcription binding sites, matched or unmatched motifs, and DNase footprint with DNase peak. On the other hand, rs12139042 was predicted to either disrupt a conserved miRNA site or create a new miRNA site. By altering miRNA binding sites, the particular SNP may hamper the regulation of the mTOR protein by miRNA.This study has several limitations. For instance, due to variable penetrance, some disease- causing alleles do not manifest into a detectable phenotype, and therefore phenotypes cannot be entirely predicted based on genotype. Moreover, a single nsSNP is unlikely to provide insight into the phenotype of complex genetic traits like diabetes, heart disease etc. In addition to that, a common mutation may have a variable expression and diverse effects in different organs and tissue types of the body . TherefoIn this study, several different bioinformatics tools were combined to screen for the most deleterious SNPs located within the mTOR protein. In total, eleven nsSNPs were filtered out to be the most high-risk as they affected the native protein’s structure and thereby function. Among these mutations, R619C, A1513D and T1977R were shown to cause structural damage to the protein while in addition to these three, L509Q, Y1151C, R1161G, R1616C and N2043S were also found to confer destabilizing effect over the protein. Furthermore, due to their position within interacting regions of mTOR, mutations like L509Q, R619C and N2043S may affect mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. Previous studies have associated several of these mutations with different cancer types however, D944V and N2043S have not been linked with any cancer yet, although they have shown disrupting structural consequences over the protein. These SNPs along with non-coding SNPs predicted to have detrimental effects on the protein structure, need to be further validated through laboratory experiments to confirm their roles in the pathogenesis of related diseases. The findings can serve as an important guide for the development of potential diagnostic and therapeutic interventions targeting these functional SNPs. All in all, this study has several promising prospects. One prospect is- based on these findings a guideline can be prepared to detail all the deleterious SNPs within mTOR which can potentially increase the risk of cancers and other disease susceptibilities. Another prospect is to perform extensive population-based studies involving clinical investigations to characterize the phenotypic effects of these genetic variations on the human population. And lastly, it will also be interesting to investigate the homozygosity and heterozygosity dependent pathogenicity level of these disease-associated mutations.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.S6 Fig(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.S8 Fig(TIFF)Click here for additional data file.S9 Fig(TIFF)Click here for additional data file.S10 Fig(TIFF)Click here for additional data file.S11 Fig(TIFF)Click here for additional data file.S12 Fig(TIFF)Click here for additional data file.S13 Fig(TIFF)Click here for additional data file.S14 Fig(TIF)Click here for additional data file.S15 Fig(TIF)Click here for additional data file.S16 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of “binding protein conformations” in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such “binding conformations” for proteins and provides a big data approach to the conformational selection mechanism. One of the frontier domains of biological research is to determine which small molecules, such as substrates or pharmaceuticals, are more likely than other small molecules to bind on a protein. Among the vast amount of small organic molecules present in living cells, only a small fraction—sometimes only a single chemical species—will normally bind to a specific protein. This specificity of protein:ligand is a central concept in chemical biology and in biochemistry, and the basis of virtually all projects in small molecules drug discovery. Yet, major questions remain on the mechanisms of protein:ligand specificity.The widely accepted view of protein:ligand interactions is that a protein cycles through multiple conformations, and that a few of these protein conformations will be bound by the small molecules. Modern biophysical tools such as virtual docking , that prIn our previous work we have performed massive systematic computational characterization of protein:ligands interactions for a very large number of protein conformations and small molecules. We have shown that we can identify the small number of protein conformations that will be “selected” for binding by their ligands. In principle these “binding” conformations will lead to free energy minima in the (protein + ligand) complex free energy hypersurface. In our research we are aiming to understand why these rare apo-conformations possess this capacity to bind their ligands, while the vast majority of the other protein conformations do not. Being able to successfully identify and predict the properties of binding conformations that render them able to bind their ligands would be a transformational change in our understanding of conformational selection, in that it will open the door to the prediction of which protein conformations should be used in the drug discovery pipeline that relies on computational docking. That will massively reduce the enormous cost of docking, by focusing on these binding confirmations alone. We also will answer several very important fundamental questions, beyond the nature of the protein properties responsible for conformational selection: are these properties similar for all proteins? Are they similar for classes of structurally close proteins? Or are they different from all proteins? Are these properties related also to the chemical properties of the ligands, as hypothesized behind most docking approaches? This work contributes to such characterization of what properties of an apo-protein conformation leads to conformational selection.The fundamental approach is relatively simple: from our previous work we already  know whiIn addition, there are fundamental issues that are specific to the field of Big Data analytics that we aim at addressing in this work. Most of the real-world biomedical datasets suffer from statistical ill-conditioning issues such as class imbalance problem , which aAnalysis of Variance (ANOVA) is a statistical technique to calculate the linear dependency between each feature and the target variable and select the features with the highest F-values . The ANOX and Y, that determines the amount of information obtained about one variable, through the other random variable [Y and the conditional entropy H(X|Y) of X with respect to Y is defined as:Mutual Information (MI) is defined as a measure between two random variables variable . In infop is the joint probability density function.p(x|y) is the posterior probabilities of X given Y.p(x) is the probability density functionI [From Equations (1) and (2), mutual information I  can be dX and Y are independent of each other, i.e., if they are unrelated, then their MI value is 0. Similarly, if X and Y are dependent on each other, then their MI value is 1. Here independent implies that no information of Y can be obtained using X and dependent implies that we can determine X from Y or vice-versa. MI measure for the physio-chemical features can be determined as follows:Compute the MI to measure the dependency between the physio-chemical features vector and the target variable for all features.The features are then ranked based on their MI values.The top K features with highest MI values are retained where K is defined by the user.Using Equation (3), if Thus, MI explains the unique information present in the physio-chemical protein features that assists in protein conformation selection process.ENTR), one of the measurements retrieved from the plots is the probability distribution p(l) of the diagonal line on the RQA plot is defined as:Recurrence Quantification Analysis (RQA) is a numerical data analysis and statistical technique that is used for the study of non-linear dynamical systems . The tecN is the number of points on the state space trajectory– l is the length of the diagonal line in the RQA plot– We evaluate the RQA-based entropy measure to understand the entropy with respect to time-space evolution of protein conformations and its relationship to probability of discovering potential binding conformation.y be the binary outcome indicating failure/success with values labeled as 0 or 1, respectively, and p be the probability of y = 1 as p = 1. Conversely, y = 0 can be expressed as 1 − p. LR then models the outcome y based on the linear combination of the independent data variables b1, b2, …, nb and their respective parameter/weight values A1, A2, …, An via maximum likelihood method as given by:Logistic regression (LR) is a probabilistic statistical ML technique used to perform predictive analysis on data that is categorical or has binary classes. The main objective of LR is to determine the best fitting model that describes the relationship between a dependent binary variable against a group of independent variables . Let y bLR technique is used in this work for supervised classification and prediction of potential binding vs. non-binding protein conformations selection.Synthetic minority Over-sampling Technique (SMOTE) is a well-liked method which helps in solving the class imbalance problem by creating new artificial data points rather than by over-sampling with replacement of the minority class [Select K neighbors.Oversampling of the minority class is carried out by taking the difference between the feature vector and its nearest neighbors.Multiply the difference obtained in the last step by a random weight between 0 and 1, and then add it to the feature vector under consideration. This causes the selection of an arbitrary point along the line segment between the two specific features.ty class . The algty class ,13,14:SeThis approach of the algorithm coerces the decision region of the minority class to be more general and therefore results in a desired well-balanced dataset.Gaussian Naive Bayes Classifier (GB) is a conditional probabilistic ML technique that is widely used for supervised data classification. According to Bayes theorem, the posterior probability can be expressed as the product of prior probability and likelihood as described by:P(y|x) is called the posterior probability. It gives the probability of y given that the data point x is true.P(x|y) is called the likelihood. It gives the probability of point x given that the data point y is true.P(y) is called the prior probability of y. It gives the probability of y being true across all data points.P(x) is the probability of data point x averaged over all of the value of y.Using Equation (6), posterior probability can be calculated for different features in the dataset. In case, when the features in the dataset has continuous values then the likelihood of the features is assumed to be Gaussian  and, henSince GB technique is probabilistic in nature, it is used in this work for supervised classification and prediction that is biased towards identification of potential binding protein conformations.The K-Nearest Neighbors (KNN) algorithm is a non-parametric ML technique that is widely used for its easy interpretation and low calculation time for classification and regression problems. Given a testing example, the KNN algorithm directly searches through all the training examples by calculating the distances between the testing examples and the training dataset in order to identify its nearest neighbors and give a classification output . The Eucx is the new data pintTrue Positive (TP): The case where the classifier correctly predicts ‘binding’ between the ligand and the target protein. (Class 1 right predictions)True Negative (TN): The case where the classifier correctly predicts ‘No binding’, i.e., the ligand and the target protein did not bind. (Class 0 right predictions)False Negative (FN): The case where the classifier predicts ‘No binding’, but the ligand and the target protein did bind. (Class 0 incorrect predictions)False Positive (FP): The case where the classifier predicts ‘binding’ but the ligand and the target protein did not bind. (Class 1 incorrect predictions)In the ML supervised learning domain, confusion matrix is often used to represent the summary of classification or prediction results. The confusion matrix terminology for a binary classification has four cases that are used to interpret the classification/prediction results as follows:Where class 0 refers to the non-binding conformations and class 1 denotes the potential binding conformations.Input the dataset into the ML framework followed by the feature selection methods: (i) Analysis of variance (ANOVA), (ii) Mutual Information (MI) and (iii) Recurrence Quantification Analysis (RQA) are applied to the original dataset.The features identified as significant information pertinent to protein conformation binding are ranked based on F-value, MI score and Entropy using the ANOVA, MI and RQA methods, respectively.A feature scoring table is created based on the features ranks obtained from the three feature selection methods.A new dataset is then created based on the feature scoring table by selecting a common subset of features across the three feature selection methods.The LR classifier is applied to both the original dataset and the new modified dataset which is more biased towards class 0 samples. The identified class 0 (TN) and class 1 (TP) samples are recorded in the classification results 1.A new training dataset is created by applying the SMOTE algorithm to the original dataset and the new modified dataset.The SMOTE Algorithm performs random undersampling of class 0 samples based on the classification results of the LR classifier and oversampling of the desired class 1 samples. This step helps in tackling the class imbalance problem and also maximizes the detection rate of class 1 or active binding drug confirmation samples. For consistency, the size of the new training dataset is kept the same as the size of the original training dataset.After applying the SMOTE algorithm, the KNN and GB classifiers are applied to the new training dataset. The classification is performed to identify the binding conformations and non-binding conformations. The results from both the classifiers are recorded in classification results.Lastly, the True Positives (TP), i.e., binding conformations and the False negatives (FN), i.e., Inon-binding conformations obtained from the KNN and GB classification are used in calculating the Enrichment Ratios using the Enrichment ratio framework, in order to validate the ML prediction framework. The results from the Enrichment ratio framework are recorded.The proposed methodology aims to identify the recurrent unique physio-chemical protein features observed in the target proteins ADORA2A, ADRB2 and OPRK1 considered in our study. For this, we combine the feature selection techniques discussed in ADORA2A: The dataset has 50 attributes and consists of 2998 molecular conformations among which 851 molecular conformations are “binding” and 2147 molecular conformations that are “non-binding”.In our work, proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1) were used for experimental validation of our proposed methods. The conformations of the three proteins have been thoroughly characterized, and the protein conformations that i) will bind to ligands (binding conformations) and ii) will not bind to ligands (non-binding conformations), are known and have been documented and published [ADRB2: The dataset has 51 attributes and consists of 2565 molecular conformations among which 156 are binding and 2411 molecular conformations are non-binding.OPRK1: The dataset has 50 attributes and consists of 2998 molecular conformations among which 138 molecular conformations are binding and 2862 molecular conformations are non-binding. In order to validate the ML protein conformation selection/prediction framework, the TP and FN predictions from the ML prediction framework described in All “Filters” in the tables and text below refer to the compound selection methods described in The ML approaches discussed predict a binding/non-binding property for protein conformations without a specific “score” or a way to otherwise select rational subsets of the true positive (TP) and false negative (FN) predictions. However, in order to calculate the enrichment ratios as described in Equation (9) through (11), we have to select different subsets of the TP and FN results. In order to do this, we have chosen to base the subset data selection on the predicted protein:ligand interactions energies that were calculated and published previously . The ratFilter A: Takes all TP and FN conformations predicted by ML algorithms, and select a subset of a X% of the lowest energy for each individual conformationFilter B: Takes all TP and FN conformations predicted by ML algorithm and takes a random Y% of those conformations and returns back to filter A.Filter C: Takes all TP and FN conformations predicted by ML algorithm, sorts conformations by binding energy and takes a random X% of conformations with the lowest protein:ligand binding energyFilter D: Takes all TP and FN conformations predicted by ML algorithm and uses the maximum binding energy calculated from filter C and selects all binding energies inferior to that energy.protein:ligand binding process. The enrichments shown in In this paper, we extended the use of the two-stage sampling-based classifier framework with additional feature scoring and feature selection methods in conjunction with Enrichment ratio framework, to identify the unique physico-chemical attributes of the binding conformation and based on those attributes understand what makes a protein conformation more conducive to There are however several challenges that limit, for the time being, the application of this approach to any protein conformation ensemble a priori. One limitation is that, as shown in In our opinion, this suggests that there is a need to continue this work with (i) other proteins for which (preferably many) known ligands exist, to see how diverse are the physico chemical features in each set, and (ii) using different physicochemical descriptors that the 40 descriptors used here. The physico-chemical descriptors used here are well validated to quantify global protein properties and discriminate between global conformation, but it may be advantageous to use physic-chemical features that are limited to the binding sites of the proteins, where the interaction of the protein with its ligands are taking place. This is not however a trivial task. However, the fundamental result of this current work, that apo-protein conformations possess properties that correlate with conformational selection, makes this effort worthwhile.Another challenge of this approach is a consequence of the severe class imbalance of the data, which contain much more conformations that are not selected by the ligands than conformations that are selected by the ligands. As shown in The motivation of this work is to understand the protein properties responsible for conformation selection. Applied to novel target proteins, this approach yielded an enrichment ratio of ~11 (ADORA2A) to 20 . Using feature scoring, we observed 5 common physio-chemical descriptors between ADORA2A and OPRK1, 1 common descriptor between ADORA2A and ADRB2 and 3 common descriptors between ADRB2 and OPRK1. No common physio-chemical descriptors were found between ADORA2A, ADRB2 and OPRK1.