Protein complexes constitute the primary functional modulesofcellular activity. To respond to perturbations, complexes undergochanges in their abundance, subunit composition, or state of modification.Understanding the function of biological systems requires global strategiesto capture this contextual state information. Methods based on cofractionationpaired with mass spectrometry have demonstrated the capability fordeep biological insight, but the scope of studies using this approachhas been limited by the large measurement time per biological sampleand challenges with data analysis. There has been little uptake ofthis strategy into the broader life science community despite itsrich biological information content. We present a rapid integratedexperimental and computational workflow to assess the reorganizationof protein complexes across multiple cellular states. The workflowcombines short gradient chromatography and DIA/SWATH mass spectrometrywith a data analysis toolset to quantify changes in a complex organization.We applied the workflow to study the global protein complex rearrangementsof THP-1 cells undergoing monocyte to macrophage differentiation andsubsequent stimulation of macrophage cells with lipopolysaccharide.We observed substantial proteome reorganization on differentiationand less pronounced changes in macrophage stimulation. We establishour integrated differential pipeline for rapid and state-specificprofiling of protein complex organization. Large-scaleprotein–protein interaction (PPI) studies using affinity purification-massspectrometry have demonstrated that almost all proteins participatein complexes,3 and we have observed thatin cofractionation studies, the majority of the total proteome massis assembled in stable macromolecular protein complexes.4 The assembly state of numerous protein complexesas well as their abundance dynamically changes to respond functionallyto specific environmental stimuli. To better understand the cell’sfunctional state, we require methods that can provide quantitativeand context-dependent snapshots of the global organization of proteincomplexes in a way analogous to what has been achieved in more standardproteomics approaches aimed at the quantification of expressed proteins.Methods such as affinity purification or proximity labeling combinedwith mass spectrometry have provided deep maps of the protein interactionspace within static cellular contexts5 or alternatively,descriptions of changes for limited numbers of protein complexes inperturbed systems.8 However, practical global methods aimed at monitoringcomplexes in many conditions have been difficult to achieve at a scaleconsistent with large-scale experiments needed to address complexbiological questions.The field of proteomics has become increasinglyinformative fromthe perspective of biology as the technology has transitioned frominitially generating qualitative lists of detected proteins towardquantitative assessment of the state of the proteome over many experimentalconditions in complex experimental designs.13 have shown substantial promise as an unbiased strategy to monitorthe composition and variations of the protein complex landscape. CoFrac-MSrelies on the biochemical fractionation (frequently SEC - size exclusionchromatography) of native cell protein extracts combined with identificationand quantification of proteins inferred by bottom-up LC-MS/MS analysisof sequential fractions. The established data analysis concept17 rests on the idea that the identity of protein interactions or,further, the composition of protein complexes can be inferred by reconstructingand correlating the elution patterns of individual proteins acrossthe SEC fractionation space. Where two or more proteins coelute, wetake this as evidence of a protein interaction or complex, evidencethat has to be further supported by using statistical filtering andthe inclusion of orthogonal information, e.g., prior knowledge thatthe respective proteins can interact.4 Inprinciple, such methods have the attractive property that they cancapture a quantitative and contextual snapshot of the proteome-wideorganization of proteins in modules for any given biological samplefrom which native protein extracts can be prepared. Comparative studiesemploying this analysis concept have demonstrated deep biologicalinsights, that would have been difficult to achieve with other methods,such as the conservation of protein complex/interaction organizationacross metazoans18 and across mammaliantissues,19 the reorganization of proteincomplexes as a function of cell cycle progression20 interactome disassembly during apoptosis,21 the architecture of the TNF–receptor signaling complex,22 the organization of ribosomes into polysomes,23 and the large-scale characterization of RNAbound protein complexes.24 While theseand other studies have demonstrated the potential of the approach,the number of applied biology studies published that employ CoFrac-MSas their basis remains relatively modest compared to more standardproteomics approaches. We suggest that the explanation for the underutilizationof this apparently informative approach lies in the massive measurementresources required to complete a statistically well-powered multiconditioncomparative experiment. Published studies have required weeks to monthsof mass spectrometer measurement time, and even with such a bruteforce approach the number of biological conditions and experimentalreplicates analyzed is limited. In the course of preparing our manuscript,Havugimana and colleagues proposed a method to scale such analysisusing multiplex isobaric labeling.25 Asecond barrier is the difficulty in extracting biologically meaningfulinformation from the complicated high dimensional data produced bydifferential CoFrac-MS studies. We and others have proposed severalcomputational strategies including an approach based on differentialchanges in protein SEC features between conditions (CCprofiler),20 an autocorrelation-based approach to detectrewiring of individual proteins across conditions (PrInCE),15 a PPI network centric approach that accountsfor changes at multiple levels (SECAT),16 and a Bayesian framework to identify alterations in protein complexes(PCprophet).17 However, a data analysispipeline that can simultaneously perform statistical comparisons ofknown protein complexes across multiple experimental conditions whilealso providing hypothesis free evaluation of evidence for proteincomplex remodeling at the individual protein level has not yet beendescribed. We suggest that an integrated method combining increasedmeasurement throughput with an integrated data analysis pipeline wouldenable the concept of CoFrac-MS to become broadly and routinely applicablein life science research.Methods based on cofractionation of nativeproteome extracts coupledto mass-spectrometry26 employs Data Independent Acquisition(DIA/SWATH) mass spectrometry enabling reproducible, robust, and sensitivequantification of peptides across protein complex fractions and experimentalgroups. Our analysis software CCprofiler uses prior protein connectivityinformation from protein complex or PPI databases to generate andexecute targeted protein complex queries to detect complexes whilecontrolling the error-rate using a target-decoy based statisticalmodel.27 This SEC-SWATH-MS strategy wasused as the starting point for the developments reported in this study.We recently introduced a number ofadvances to the CoFrac-MS approach.SEC-SWATH-MS28 and uponfurther induction of an inflammatory response via lipopolysaccharide(LPS) stimulation29 in the differentiatedmacrophages.We present an integrated experimental and computational workflowfor the global assessment of protein complex reorganization in perturbedsystems. Our approach relies on DIA/SWATH analysis of SEC fractionsusing short gradient chromatography that increases throughput by ∼1order of magnitude, achieving a measurement capacity of ∼1biological sample per day with similar information content comparedwith prior low throughput methods. The increase in throughput facilitatesthe comparison of multiple experimental groups with multiple biologicalreplicates. To deal with this increase in complexity and to maximizethe information content discernible from the data, we developed statisticalmethods to compare the data from several perspectives that we referto as (i) assembled mass fraction, where we assess whether a givenprotein is shifting between monomeric and assembled states, (ii) protein-centric,where we detect and differentially quantify individual protein SECfeatures between conditions, and (iii) complex-centric, where we quantifychanges in protein complexes detected by a hypothesis driven approach.We benchmark our workflow with respect to a typical lower throughputstrategy and then demonstrate its performance by investigating rearrangementsin the protein complex landscape of THP-1 human monocytic precursorcells when undergoing a phorbol ester induced differentiation intoa macrophage-like phenotype6 and 1.2 × 106 cellsper mL at 37 °C in a 5% CO2 incubator. 1.5 ×106 THP-1 cells were differentiated when supplemented with50 ng/mL PMA  for 48 h, and, when stated, the differentiationtreatment included a 24 h stimulation with 100 ng/mL LPS .The suspension cells or differentiated adherent cells were washedwith PBS  and were sedimented in a pellet by centrifugationat 300g kept at 4 °C. The cell pellets wereimmediately snap-frozen in liquid nitrogen.The human monocytic cell line THP-1  was cultured and expanded in RPMI 1640 media  supplemented with 10% FCS  and 1%penicillin/streptomycin  and kept at a confluencybetween 0.5 × 103VO4, 1 mM PMSF, 1×protease inhibitors , 1× benzonase ),and incubated for 5 min at room temperature. The lysates were centrifugatedat 13000g for 10 min to remove insoluble materials.The extracted proteins were reduced at 5 mM TCEP for 30 min at 37°C while shaking at 500 rpm and subsequently alkylated in 10mM iodoacetamide for 30 min at 37 °C. The proteins were precipitatedovernight in 100% acetone at −20 °C and pelleted by a30-min centrifugation step at 4 °C. The protein pellets werethen resuspended in 1% SDC, 8 M urea in 0.1 M ammonium bicarbonateand sonicated for 10 min. The proteins were diluted to 0.1 M ammoniumbicarbonate and digested overnight with trypsin  at37 °C with a protein-to-enzyme ratio of 50:1. The digestionswere stopped with 50% TFA, and the SDC was removed by two centrifugationsteps of 10 min each at 16000g. The peptides weredesalted and cleaned-up using C18 columns and were resuspended in 5% acetonitrile, 0.1% formic acid with iRTpeptides .The proteinswere extracted from the frozen cell pellets by lysing the cells with1% SDC  in HNN Buffe pH 7.8 . A 200 μg portion ofpeptides was injected into an Agilent Infinity 1260  and 1290  systemand separated on a 25 cm long C18 reverse-phase column (YMC Triart)with 3 μm particle size and 12 nm of pore size. The peptideswere separated at a flow rate of 12 μL/min by a linear 56 mingradient from 5% to 35% Buffer B  against Buffer A  followed by a linear4 min gradient from 35% to 90% Buffer B against Buffer A and 6 minat 90% Buffer B. The resulting 36 fractions were pooled into 12 samples.The buffer of the pooled samples was evaporated using vacuum centrifugationat 45 °C, and the resulting 12 samples were resuspended in 2%ACN, 0.1% FA with iRT peptides (Biognosys).30 THP-1 cells were thawed and lysed in mild conditionsby homogenization with a lysis buffer composed by 0.5% NP-40 detergentand protease and phosphatase inhibitors ). Cell debrisand membranes were removed by 15 min of ultracentrifugation , and the detergent was removed by a 30 kDamolecular weight cutoff membrane and exchanged with the SEC buffer. The samples were concentrated fora final protein concentration between 7 and 12 μg/μL.After 5 min of centrifugation at 16900g at 4 °C,the supernatant was directly injected to a Yarra-SEC-4000 column . 0.8 mg of native proteome extract  was injectedfor each SEC run at 4 °C with a flow rate of 500 μL/min,for a total chromatographic time of 30 min. Fraction collection wasperformed in the retention time window from 10 to 26 min, at 0.25min per fraction, for a total of 64 fractions collected.Proteincomplex fractionation was performed as previously described.Supplementary Table 18).Themolecular weight calibration curve for SEC fractionation was obtainedby running a protein standard mix  before each sample injection were loaded and concentrated on the filters throughcentrifugation, until complete removal of the SEC buffer.Sampleprocessing for bottom-up analysis of SEC fractions was performed on96-well plate MWCO filters .Proteindenaturation and reduction were obtained incubating the samples at37 °C for 30 min with 5 mM of TCEP in 8 M urea/20 mM ammoniumbicarbonate (AMBIC) (pH 8.8). Alkylation of cysteine residues wasperformed by adding a final concentration of 50 mM IAA/20 mM AMBICand incubating in the dark and at room temperature for 1 h. Afterthe reaction, the plates were centrifuged to remove the urea bufferand washed three times with 20 mM AMBIC. Protein digestion was carriedout at 37 °C for 16 h, adding to each well 1 μg of trypsin and 0.3 μg of lysyl endopeptidase .The resulting peptides were collected by centrifugation, and the plateswere washed once more with 100 μl of ddH20.32 coupled to an AB Sciex TripleTOF 6600 instrument equipped with a NanoSpray III ion source (Sciex).The samples are loaded in Evotips , afterresuspension in solvent A  and the addition of iRTs peptides (Biognosys) ina ratio 1:100 for the retention time alignment requested for SWATHacquisition. 75% of the peptide recovered from each SEC fraction wasloaded. For the loading, the C18 stage tips (Evotips) were soakedwith 100 μL of 2-propanol during the activation and the conditioningsteps. The activation step consisted of washing with 20 μL ofsolvent B , followedby conditioning with 20 μL of solvent A. Prior the sample loadingstep, 10 μL of solvent A was added on top of the tips, ensuringthat the tips remain wet during the loading step. For each steps,the Evotips were centrifuged for 1 min at a speed of 700 g for theelution of the solvents. The last step  was performedusing 100 μL of solvent A, and the loaded tips were added with200 μL of solvent A for preserving the samples during the entireinjection of the batch.DIA/SWATH analysis of the peptide fractionswas performed on an Evosep One system 32 The ESI coupling was obtained using a Nano Source Emitter StainlessSteel Nanobore 1/32 (Thermo Fisher Scientific).The separation of peptides was performedselecting the “60 samples per day” method, consistingof 24 min of total cycle time, for 21 min of gradient length, and3 min of overhead time at a flow rate of 1 μL/min. A partialgradient is applied (0–35% solvent B) in order to elute thepeptides from the Evotip by two couples of low pressure pumps. Thepeptides were then pushed in a C-18 nanoConnect LC column using a high pressure pump and solvent A.The ESI tuningparameters were the following: spray voltage, 2800V; ion source gas flow (GS1), 16; curtain gas flow (CUR), 35; interfaceheater temperature (IHT), 100 °C; declustering potential, 100.The Evosep system was controlled by Axel Semrau Chronos software, while the mass spectrometer acquisitionsoftware was Analyst TF 1.7.1 (Sciex).33 was performedfor the quantitative analysisof the 576 SEC fractions (64 fractions per sample) obtained from the9 SEC experiments. A SWATH scan was performed using an updated schemeof 64 variably sized precursor coisolation windows,34 covering similar precursor densities (in terms of numberand intensity) within all SWATH windows. The SWATH windows cover theprecursors ions in the range of 350–1500 m/z and 350–1500 in the MS2 SWATH scans, andthe accumulation time was 100 ms for the MS1 and 20 ms for each SWATHwindow, resulting in a cycle time of 1.38 s. For fragmentation, arolling collisional energy with a collisional energy spread of 15eV was applied.Data-independent acquisition(SWATH/DIA) mass spectrometrym/z range of360–1460 and an accumulation time of 250 ms was followed by20 MS2 scans with m/z ranges of50–2000 and accumulation times of 100 ms. The dynamic exclusiontime was set to 20 s.The 12 highpH fractioned peptide samples were separated on an Eksigent nanoLCUltra AS2 1D Plus and expert 400 autosampler system  coupled to a TripleTOF 5600 ion source through a NanoSpray IIIion source by using a Data Dependent Acquisition (DDA) scheme. The20 cm long nanoLC column was packed in house using a 75 μm innerdiameter PicoFrit emitter  with Magic C18 AQ3 μm, 200 Å particles. The separation was performed atroom temperature at a flow rate of 300 nL/min. All of the LC solventswere all of mass spectrometry grade. The LC solvent A was composedof 98% water, 2% acetonitrile, and 0.1% formic acid, and LC solventB was 98% acetonitrile, 2% water, and 0.1% formic acid. The peptideswere eluted over 120 min, with a linear gradient from 5% to 35% LCsolvent B. Collision energy and ESI parameters were for DIA analysis.One MS1 scan with an 35DDA-MSdata acquired from peptide fractionation of the full THP-1 cell lysates(see above) were processed for the SWATH library generation followingthe protocol previously described.36 A widerpeptide-level FDR cutoff  was chosen in order to increase sensitivity for the recoveryof true positive peptide signals.MS spectra were searched for peptide matches against the human UniProt/SwissProtreference database  usinga Comet 2018.01 rev. 0 MS/MS search engine. The search was carriedout using trypsin cleavage, 30 ppm precursor, and 0.05 Da fragmention mass tolerance, carbamidomethyl (C) as static and oxidation (M)as variable modification, and a maximum of 2 enzyme missed cleavages.The results from the search were statistically scored using PeptideProphet  and iProphet  of the Trans-Proteomic Pipeline (TPP v5.0.0 POLAR VORTEXrev 0), filtering the results at 1% peptide FDR (0.815939 iprob) asdetermined using the tool Mayu.n or yn ion series wereselected, with an m/z range of 350–2000and aa fragment charge states 1–2. The final library containsquery parameters for 506,717 precursors of 73,007 peptides mappingto 9375 protein groups. Moreover, to the spectra consensus libraryreverse decoys  were generated for theFDR scoring provided by the SWATH/DIA data analysis workflow.The resulting spectra werethen gathered for the generation ofthe consensus spectra library using SpectraST including the retentiontime calibration. The 6 most abundant fragment ion transitions perprecursor from the b38 PyProphet and TRIC39 workflow. Initially,one global classifier was trained on a subsampled set of SEC fractionsacross the experiment using pyProphet-cli.40 Peptides from all fractions were then quantified and scored usingthe pretrained scoring function using pyProphet and TRIC. The HeLabenchmark data set was analyzed with Spectronaut v14 using a previouslypublished HeLa CCL2 spectral library.41For the THP-1 experiment, theDIA/SWATH data collected from the analysis of SEC fractions were analyzedthrough peptide-centric analysis, querying 506,717 fragment precursorsfrom the sample-specific peptide library generated (see above) inthe SWATH MS2 spectra, using an OpenSWATH v2.1The first differential analysis module inCCprofiler is tailored toward detecting proteins that differ in theirglobal assembly state, meaning that the relative distribution betweenmonomeric and assembled protein mass is different across the conditions.Since this module depends on the assignment of the fractionation dimensioninto a monomeric and assembled range based on the monomeric molecularweight of each protein, the analysis is currently only available forSEC data sets and requires both a molecular weight calibration ofthe fractions and a monomeric molecular weight annotation of the measuredproteins. The cutoff between the monomeric and assembled SEC rangeis set at the fraction corresponding to two times the expected monomericmolecular weight of a protein. Based on this initial division of theSEC dimension, the assembled mass fraction (AMF) of each protein canbe estimated by the fraction of the detected MS signal in the assembledmass range relative to the total globally detected signal:t testfor significance estimation is not applicable. Instead, CCprofilerapplies a beta-regression model and p-value estimationby a likelihood-ratio test to derive significance estimates . Multiple testing correction is performed by Benjamini-Hochbergadjustment of the derived p-values.42 Proteins with significant adjusted p-valuesand large AMF differences are indicated to have a different proportionof individual proteins associated with higher order assemblies acrossthe conditions. Notably, this information is derived independent fromany feature  detection and does not require knowledgeof the protein’s exact interaction partners.A change in AMF is subsequently estimatedby the difference in the mean AMF across conditions:30 Here, we extend the protein-centricanalysis concept to enable differential assessment of distinct proteinassembly states. To achieve consistent protein feature  detection across conditions and replicates,peptide-level traces are first integrated by summing the intensitiesacross all samples in the provided tracesList. The integrated tracesare subsequently used for protein-centric feature finding, applyingrandom peptide assignments as a decoy model for p- and q-value estimation.30 Each protein can thereby be assigned to potentially multiple distinctassembly states, as indicated by the detection of multiple uniqueprotein features. Following this initial protein feature detection,a differential analysis is performed to compare the signal intensitywithin each protein feature across conditions.To further gain insights intodistinct protein assembly states, we have previously introduced theprotein-centric analysis concept for CoFrac-MS data within a singlecondition.p-values are estimated by comparing the summed intensitiesacross conditions by a nonpaired t test. If no replicatesare available, p-values are estimated by comparingeach fraction within a feature by a paired t testacross the conditions. (v) To subsequently derive protein-level information,the peptide-level tests are aggregated as follows: (1) protein log2-fold-changesare derived from the median log-2-fold change across all detectedpeptides of the protein, and (2) protein p-valuesare estimated by determining the fold-change adjusted median p-value and applying a beta distribution as described byTeo et al.43 and Suomi et al.44  Peptide-level intensities arecomputed for each protein feature and sample. Missing values in singlefractions, replicates, or conditions are imputed by uniformly samplingvalues between zero and the minimum detected signal of a peptide.The peptide intensity of one feature is then calculated by summingthe intensities of all fractions across the corresponding proteinfeature range. (ii) The mean intensity across all replicates withina condition (specified by the design matrix) is calculated. (iii)The log2-fold-change between conditions is calculated based on themean feature intensities. (iv) If replicates are available, p-values are performed. To assess whether thesignal within a protein feature is changing because of a global changein the protein’s expression or due to a rearrangement of theproteins relative distribution across different assembly states, anadditional analysis step is available in CCprofiler. Here, the relativefeature-specific mass fraction (FMF) is estimated by the fractionof the detected MS signal in the feature-specific mass range relativeto the total detected signal:p-value estimation by a likelihood ratio test to derivesignificance estimates for the change in FMF across conditions  for each complex query is considered for scoring and FDR estimation.After filtering for q-values , the complexfeatures are appended by secondary features with high correlationvalues (peak correlation 0.7). These secondary features can for exampleentail potential subcomplexes or complex variants.30The final analysis module in CCprofiler is focused on complex-centricdetection and differential assessment of protein complexes. We havepreviously introduced the basic concept of complex-centric analysisfor CoFrac-MS data of a single condition.p-values.42Following this initial protein complex feature detection,a differential analysis step can be performed to compare the signalintensity within each complex feature across different conditions.The analysis concept is analogous to the differential analysis strategyimplemented on the level of protein features (see the previous section).The initial differential testing is performed on the peptide level,while results are subsequently aggregated on the protein level. Forcomplex-centric analysis, the protein-level results are additionallyaggregated to the complex level, again following the same strategyas compared to aggregation from the peptide to protein level. Finally,multiple testing correction is performed by a Benjamini-Hochberg adjustmentof the p-Value estimation for AMF and FMF differenceswas performed by first transforming the AMF and FMF (y) to values between zero and one, while excluding the extremes (0and 1):46n denotes the samplesize, which was six for the presented data set. The resulting y′ values were used for fitting a beta-regressionmodel with the betareg R package with default parameters.46 The lrtest function of the lmtest R package was subsequently usedfor p-value estimation by a likelihood-ratio testwith default parameters. Multiple testing correction was performedby the p.adjust function of the stats base package, using the “fdr”method corresponding to correction by Benjamini-Hochberg.42p-values were aggregated to the protein-level by applyingthe strategy presented by Teo at al.43 andSuomi et al.44 First the median of peptide-level p-values is used as a score for each protein taking thedirection of change into account. The protein-level significance ofthe detection is subsequently calculated using a beta distribution.44 The same strategy is applied to aggregate protein-level p-values to the complex level. Multiple testing correctionis performed by the p.adjust function of the stats base package, usingthe “fdr” method corresponding to correction by Benjamini-Hochberg.42Peptide-level All R-scriptsfor the CCprofiler analysis are openly available on github. The followingsection provides a summary of the most important processing stepsand the selected parameters for the presented analysis.48 a method initially implemented for microarrayanalysis using pairwise loess curve fitting cycling through all possiblepairs several times. Low-confidence peptides were subsequently removed,keeping only peptides with (1) at least three consecutive detectionsacross any replicate, (2) at least one high correlating sibling peptide(maximum correlation ≥ 0.5), and (3) a good average siblingpeptide correlation (≥0.2). Protein quantification was performedby summing the top two most intense peptides consistently across allreplicates.Dueto the very low molecular weight of later SEC factions, the data werelimited to fractions 1 to 49 for CCprofiler analysis. Missing peptideintensity values  were imputed by a spline fitacross the SEC dimension. After missing value imputation, peptideintensity values were normalized across conditions and replicatesby applying a cyclic loess normalization,p-value ≤ 0.05were required.To determine proteins with a significant changein their assemblystate across conditions, a mean difference in AMF of ≥30% anda Benjamini-Hochberg adjusted p-value of 0.05 wererequired for significance in all pairwise analyses. To determine proteinfeatures with a significant change in their relative abundance incomparison to the total protein intensity across conditions, a meandifference in FMF of ≥30% and a Benjamini-Hochberg adjusted p-value ≤ 0.05 were required.Protein-centric analysis was performed with followingparameters:corr_cutoff = 0.9, window_size = 7, rt_height = 1, smoothing_length= 7, perturb_cutoff = ”5%”, and collapse_method = ”apex_only”.Only protein features passing the 5% FDR threshold were further considered.For the differential analysis, a minimum log2 fold-change of one anda Benjamini-Hochberg corrected 49 and StringDB.50 We derived protein complex queries from StringDB version10 (9606.protein.links.v10.txt). Protein identifiers were mapped toUniprot accessions via BioMart. The interactions were filtered fora minimal combined_score of 980. We applied the ClusterONE algorithm51 for PPI network partitioning with followingparameters: d = 0.95. Weights were set to the combined_scoredivided by 1000. CORUM derived protein complex queries were takendirectly from within the CCprofiler package.30 The complex queries were combined, and decoys were generated randomlyby requiring a minimum edge distance of 3. Complex-centric analysiswas performed with the following parameters: corr_cutoff = 0.9, window_size= 7, rt_height = 1, smoothing_length = 7, perturb_cutoff = ”5%”and collapse_method = ”apex_network”. Only complex featureswith a molecular weight higher than two times the largest monomericmolecular weight of any of its participating subunits were considered.For each protein complex query, the complex feature with the highestnumber of participating subunits was selected for the FDR estimation,filtering for a maximum FDR of 5%. Secondary features were appendedto the final results based on the basis of a minimum peak correlationthreshold of 0.7. To reduce redundancy across the detected complexfeatures between different queries, features were collapsed with thefollowing parameters: rt_height = 0 and distance_cutoff = 1.25. Forthe differential analysis, a minimum log2 fold-change of one and aBenjamini-Hochberg corrected p-value of 0.05 wererequired for significance in all pairwise analyses.For complex-centricanalysis, we first defined a set of targetprotein complex queries. This was achieved by combining queries derivedfrom CORUM52 (ii) directsample loading onto solid phase extraction tips, omitting an offlinereversed phase-based cleanup step; (iii) a data acquisition strategycomprising a 21 min LC gradient (24 min injection to injection time)using direct loading from solid phase extraction tips and embeddedgradients to reduce overhead. This advance enabled the acquisitionof data for 1 biological sample comprising ∼60 SEC fractionsper day while minimizing loss in sensitivity;32 (iv) a DIA/SWATH acquisition strategy specifically optimized tomaintain proteome coverage and quantitative robustness for short gradientanalysis .To increase the throughput of the SEC-SWATH-MS workflow, we optimizedmultiple steps of the experimental procedure 1a. These28 and subsequently a further lipopolysaccharide (LPS) stimulationof the macrophage cells to elicit an inflammatory response29 , enabling theassignment of a monomeric and assembled SEC elution range specificfor each protein detected. The fraction of observed protein mass inthe assembled SEC elution range is represented by the assembled massfraction (AMF)  see .The dif20 In contrastto a standard differential protein expression analysis, abundancefold-changes and p-values are computed for each distinctprotein feature, thereby capturing not only changes in overall proteinexpression but also abundance changes of specific assembly states.In addition to the feature-specific differential analysis, globaldifferential assessment is performed by comparing integrated intensitiesacross the entire fractionation dimension instead of restricting theanalysis to a feature-specific range. This global analysis can essentiallybe considered equivalent to a standard  proteomicsanalysis as we are essentially summing over the signal for each proteinacross the SEC dimension effectively collapsing this dimension. Thesame strategies as for feature-specific estimation of log2-fold-changesand p-values are performed. Additionally, we providethe opportunity to compare the relative distribution of protein massacross the various detected assembly states  at various stagesof the data analysis. Using the method described for collapsing potentiallyredundant complexes, we notice that the apparent overlap of complexesbetween long and short analysis methods appears low compared to thenoncollapsed complexes; however, this is due to differences in exactsubunit detection for these complexes and gives a somewhat misleadingimpression (see Supplementary Tables 3 and 6). Overall, we recovered 70%, 77%, and 95% of the information atthe peptide, protein, and protein complex levels, respectively, whencomparing the short gradient to the long gradient method with ∼halforder reduction in protein SEC feature apparent dynamic range . Inthis comparison, we analyzed equivalent SEC fractions from a HeLaCCL2 native protein extract with either method. The DIA/SWATH datawere analyzed using Spectronaut and a previously published HeLa CCL2spectral library.ic range 3b.Supplementary Figure 4. As only the first 50 fractions analyzed were informative,we discarded the remaining fractions for the following analysis, andthese could be excluded from measurement in future iterations. Wefirst assessed the consistency and comparability of the 9 fractionationruns by performing pairwise alignments at the peptide-level and calculatedthe global correlation among all matching peptides . The results demonstrate that theSEC runs were reproducible and did not require further alignment.To enable a quantitative comparison of the respective SEC runs atthe three analysis levels, we then normalized the intensities usinga cyclic loess method . To increase the confidence for downstream analyses, we filteredout peptides which were not identified in two consecutive SEC fractions,we only kept proteins supported by more than one proteotypic peptide,and we required that the remaining proteins are supported by at leasttwo highly correlating sibling peptides . After these conservative filtering steps, the mean number of detectedproteins per SEC run was 4,013 . We nextapplied chromatographic feature finding to the concatenated data setof all 9 SEC runs and detected 5,196 protein SEC elution featuresfrom 3,335 proteins at a 5% FDR threshold, among which 911 (27%) weredetected as monomers only, while 2,424 (73%) had at least one elutionfeature in the assembled molecular weight range (>2× monomermolecular weight). Of the 3,335 proteins, 1389 had multiple detectedfeatures at different molecular weights, thus suggesting their contributionsto more than one complex .Having demonstrated that our rapid method stillprovides comparativeproteome and interactome coverage, we next turned to the THP-1 perturbationexperiment. We analyzed the 576 LC-MS/MS runs from the three experimentalconditions in SEC triplicates using the OpenSWATH computational pipelineand a THP-1 specific spectral library generated by DDA analysis of12 basic reversed phase fractions of a pool of the biological samples.It contained 84,453 peptide precursors, mapping to 9,375 proteins.The output, initially filtered with relaxed FDR thresholds beforeimport to CCprofiler, contained on average 46,146 proteoytpic peptidesper SEC run  from which we inferred 5,736unique proteins on average  across the dataset at a 10% run-specific peak-group FDR, 5% global peptide FDR, and5% global protein FDR before further FDR refinement in CCprofiler(described below). The distribution of detected peptides and proteinsas a function of SEC fraction is shown in To assess the global biological information contentof the THP-1data set, we computed the coefficient of variation (CV) for peak areasof protein SEC features over the biological replicates within experimentalgroups and compared these to the CV over all samples across groups. Supplementary Figure 10 and Supporting Information Tables 7–8). Aglobal view of the change in the AMF with respect to the experimentalcomparisons is shown in p-value less than0.05) comparing the differentiated to undifferentiated conditions(51 proteins increased AMF and 10 proteins decreased AMF on differentiation),and only 1 protein showed a significant change (decreased AMF) whencomparing the stimulated to differentiated conditions. For example, p-value = 0.003) with 32.4%of the signal for this protein observed in the assembled state. Stimulationof the differentiated cells with LPS did not produce a further significantshift in the assembly state of SHC1 with 38.4% in the assembled state. SHC1 has been directly implicatedas a signaling adaptor in monocyte to macrophage differentiation inprevious studies.54To compare between experimental groups, we appliedthe 3 quantitative modules of CCprofiler beginning with the AssembledMass Fraction (AMF) analysis. Over the 9 samples analyzed, 60–64%of the global proteome mass was estimated to be in an assembled state between SEC features for 540 proteins in the differentiatedvs undifferentiated comparison and in 30 proteins for the stimulatedvs differentiated comparison (Supplementary Tables 9–11). In the protein-centric global comparison, where the signal fromall SEC fractions for a given protein is summed and compared acrossconditions, we observed significant changes for 428 proteins in thedifferentiated vs undifferentiated comparison and 39 proteins forthe stimulated vs differentiated comparison. We then performed thefeature-specific mass fraction (FMF) comparison in which we can inferwhether a protein changes its relative distribution across differentassembly states. We detected 114 proteins in the differentiated vsundifferentiated comparison that underwent changes in their assemblystate that were not attributable to changes in overall protein quantity,and, in contrast, we could detect no proteins in this category forthe stimulated vs differentiated comparison. 55 and therefore reorganization of this complex is plausiblyfunctionally relevant in this monocyte to macrophage transition.We next applied the Protein-Centric Analysis moduleof CCprofilerto the THP-1 data set. Overall, we observed feature-specific quantitativedifferences . This resulted in 3,127complex hypotheses, from which 644 were detected; 104 were fully detected,375 were detected with at least 50% of the subunits present, and 165were identified with less than 50% of the subunits present, all witha 5% FDR at the complex-detection level . We further collapsed these 644 confidently detectedprotein complex queries to 321 likely unique protein complexes basedon subunit composition and position in the SEC dimension.26 Overall, we observed significant quantitativechanges in 17 protein complexes, composed of 73 protein subunits,in the differentiated vs undifferentiated comparison. This is in contrastto the stimulated vs differentiated comparison in which only a singleprotein complex was called as significantly different (Supplementary Tables 13–17). 56 For example, LPS stimulation in macrophageshas been shown to increase IFIT expression levels in order to enhancethe secretion of proinflammatory cytokines including TNF alpha andIL-6.57 We observe a clear signal for theIFIT complex at the expected molecular weight under the LPS stimulatedconditions and compared with only baseline amounts under the differentiatedand undifferentiated conditions. As such, the increased expressionof the IFIT proteins and their assembly into a complex are consistentwith the LPS stimulation employed in our experiment. Interestingly,IFIT1 and IFIT3 (but not IFIT2) also appear in an as yet unannotatedhigher molecular weight assembly.Finally, we applied the complex-centric module of CCprofiler tocompare the set of detected protein complexes under the various conditions.To generate a comprehensive input hypothesis-set for the complex-centricanalysis, we merged the CORUM databaseSupplementary Table 18). Asexpected, there is some redundancy between these modes of analysisbut also much complementarity as each strategy is tuned to detectdifferent aspects of protein complex reorganization. This view ofthe data underscores the magnitude difference in the response fromthe perspective of proteome organization to the chosen biologicalperturbations. Here we expect to capture changes related both to increasesin the abundance of protein complexes driven primarily by changesin the abundance of their protein subunits ”, “protein-centric (feature-specific)”,and “complex-centric” categories) as well as changesin protein assembly composition (exclusive to “protein-centric(FMF)” and “assembled mass fraction”, or combinationsof those mechanisms that appear in multiple categories). This graphalso underscores the observation that substantially more changes occurin cells that transition from the suspension monocyte state to theadherent macrophage state than in the comparison of macrophage cellsthat are stimulated to upregulate immune defenses by LPS stimulation.To obtain a global functional picture of the response to these perturbationsfrom the perspective of proteome organization, we performed a functionalenrichment analysis based on the consolidated list of proteins significantlyaltered in each element of the CCprofiler analysis. We examined the distribution of protein SEC featurepeak areasand determined that while the peak areas of protein features thatare detected at >2× the expected monomer molecular weightfollowthe same peak area distribution as all detected protein SEC features,the set of protein SEC features that match protein complexes detectedin the complex-centric analysis are shifted toward higher peak areas,indicating we are somewhat less likely to successfully call complexesfor lower abundance features . This ob41 (https://collins-lab.shinyapps.io/secexplorer_thp1/). This online tool provides the opportunity to manually query theSEC profiles of our 3 biological THP-1 conditions by providing aninteractive viewing. It enables the manual query for locally coelutingproteins to potentially identify de novo interactionsand to visualize the results from the 3 differential CCprofiler modules.In order to render the data easily accessible andviewed in depth,and enable manual query of community-based testing of novel putativeinteracting proteins supported by the presence of coelution profiles,we have made the data accessible to the community via a web portalMethods using cofractionation as a basishave promised characterizationof the state of protein complexes and their reorganization upon cellularperturbation in a global and quantitative fashion. However, with thecurrent state of the workflows, this remains a somewhat distant goalwith respect to routine application, especially for complex experimentaldesigns. While a number of studies employing this approach have demonstratedsubstantial biological insight, the general strategy has failed tobreak into mainstream use. At the outset of this study, we identifiedtwo major factors holding back progress, namely, (i) the resourcesrequired per biological sample for typical implementations of thisstrategy are not practically compatible with complex study designsincluding multiple experimental conditions with biological replicationand (ii) a lack of integrated software solutions that could performdifferential statistical analysis at all levels of interest . In this study,we address both barriers by developing an integrated experimentaland computational pipeline for rapid quantitative profiling of proteincomplex states.13 Inreal terms, the actual increase in throughput is substantially higherbecause the short gradient chromatography using solid phase extractiontips for loading and embedded gradients for separation reduces substantiallythe need for maintenance procedures such as instrument cleaning andcolumn changes that typically interrupt data acquisition blocks usingclassical long gradient methods. As such, this represents an orderof magnitude reduction in the time required to acquire data for thistype of experiment and does not require the complexity and experimentaldesign constraints associated with multiplex labeling . While the gains in throughput are criticalto the further development of this strategy, we expect commensuratebenefits to data quality as the inevitable effect of drift in instrumentperformance over time34 will be substantiallymitigated by the reduced measurement time and reduced need for instrumentmaintenance during data acquisition.32 Ina benchmarking experiment using HeLa cells, we demonstrate that theinformation content from our rapid method is comparable to that ofa standard long gradient approach, and we demonstrate in our THP-1perturbation experiment that the across-group variation in our quantitativedata exceeds the within-group variation, indicating we capture biologicalinformation. Further, since our data were acquired a number of improvementsin MS data acquisition schemes aimed at maximizing the numbers ofpeptides/proteins quantified in short gradient data have been introduced,and we expect our strategy to directly benefit from these, increasingsensitivity and protein complex coverage and reducing analysis time.61 Our study, while demonstrating the potential of increased throughputof analysis, remains limited in terms of the number of biologicalreplicates and the biological conditions evaluated. The number ofreplicates needed for well-powered analyses in more heterogeneousbiological systems  remains an open questionand will need to be investigated empirically.We present an optimized method that facilitatesthe interrogationof many biological samples in perturbation experiments with biologicalreplicates within a feasible time frame and a robust data analysispipeline. By using robust short gradient liquid chromatographic separationand DIA/SWATH data acquisition, we could reduce the MS acquisitiontime of 9 SEC runs of 64 fractions each, totaling 576 samples downto 9.6 days. This is in comparison to an estimated 50.4 days for thesame project based on the 90 min gradient and 126 min injection toinjection time that we used in the long gradient comparison for thisstudy, although we note many studies using this strategy perform 2–4h gradients for CoFrac-MS analyses.20 software pipeline,that maximized the information extracted from the more complex experimentaldesigns that are facilitated by our higher throughput method. Theseinclude the capability to assess between group differences from 3perspectives that each have their own advantages/disadvantages. Complex-centricanalysis provides the richest information on the reorganization ofprotein complexes as a function of the perturbation but is likelymissing many interesting changes because it relies on the prior informationin the form of testable protein complex hypotheses that may be incomplete.We note that several tools have been introduced recently that leveragemachine learning to define protein complex hypotheses from CoFrac-MSdata,17 and thesecould be used as input for the CCprofiler complex-centric analysis.The protein-centric strategy is free of any such assumptions and simplyasks whether a given protein feature in the SEC dimension changesbetween experimental groups. Our results show that this comparisoncan sensitively detect changes that are a proxy for changes in proteincomplex reorganization or abundance in a fine-grained manner. Theassembled mass fraction strategy similarly does not require backgroundprotein complex information and further does not require feature findingin the SEC dimension, meaning that it may detect changes in proteins/complexeswhich smear across many SEC fractions that would be missed by theother methods. While a recent meta-analysis13 suggests that the optimal number of fractions for Cofrac-MS is similarto that used in our study, it seems plausible that strategies aimedat looking for shifts of given proteins along the complex separationdimension could work with substantially fewer fractions. Such an approachhas recently been proposed where only 5 SEC fractions are used inthe context of discovering chemical probes that impact protein complexassembly states.62We made severalalgorithmic improvements, embedded in several novelmodules of our CCprofiler64 Nevertheless, while a smallernumber of changes were observed in macrophage LPS stimulation as comparedwith differentiation from monocyte to macrophage, functional categoriesrelated to innate immune response were clearly overrepresented inthe results integrated from our three level CCprofiler analysis.The biologicalperturbations that we chose induce different cellularstates that likely rely on quite different molecular mechanisms, andthis difference is reflected in our results. We observed evidencefor a significantly higher number of changes in protein organizationor abundance when comparing the monocyte state to the differentiatedmacrophage state than when comparing the unstimulated versus stimulatedmacrophage cells. This is also apparent from the unsupervised clustering,which clearly shows the undifferentiated state as clearly distinguishedfrom the differentiated and LPS stimulated states. Differentiationfrom a suspension monocyte-like phenotype to an adherent macrophage-likestate is a gross and irreversible phenotypic change that likely requiresthe remodeling of many protein complexes that are visible to our method.When integrating changes observed at the three levels of CCprofileranalysis, we see substantial alterations in proteome organizationrelating broadly to signaling, cell adhesion, and extracellular matrixrelated functions, whereas the induction of an inflammatory responsemay be better characterized as a change in signaling/activation ingiven pathways that rely more on PTMs or transient changes in protein complex assembly state that are moredifficult to detect. This observation underscores the idea that datagenerated from this approach would benefit from combination with otherdata types  where their interdependencecould be assessed.With the introduction of our rapid integrated method, we anticipatethat global profiling of protein complex reorganization in perturbationexperiments with complex experimental designs will be enabled as aprimary tool in systems biology research and beyond.


We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs  were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values −3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations  were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of −18756 kj/mol compared to the initial model of −108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were −107.09 kcal/mol, −109.42 kcal/mol, and −107.18 kcal/mol, respectively as compared to wild-type HRAS protein had −105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed  The HRAS gene is identified as the Harvey rat sarcoma viral oncogene homolog and is responsible for encoding a GTPase protein of small stature that belongs to the RAS family. A plethora of cellular processes, comprising proliferation, differentiation, and survival, are subject to regulation by this particular intracellular signalling pathway . GeneticSimilarly, in bladder cancer, HRAS mutations have been detected in 1%–2% of cases and are associated with high-grade tumours and advanced disease . IdentifThe HRAS gene and its protein product have significant roles in tumour genesis by regulating fundamental cellular processes such as growth, differentiation, and viability. In its wild-type cellular context, the expression and activity of HRAS are under the precise control of several signaling pathways, especially the RAS-MAPK axis, which is vital for cellular differentiation and growth. However, genetic mutations in the HRAS gene can disrupt the balance of the RAS-MAPK pathway, leading to unregulated cellular proliferation and neoplastic transformation . Such HRProteins exhibit nsSNPswhich can result in alterations in the amino acid sequence. It is well-established that such modifications have been linked to the initiation and advancement of cancer . These vin silico approaches to predict the effects of rare genetic variants on HRAS protein function, identify the most deleterious nsSNPs, and investigate the consequences of these mutations on the stability, flexibility, and compaction of the HRAS protein using molecular dynamics simulations. In this study, we also analyze the binding energies of the wild-type and mutant HRAS protein with docked complexes to understand the potential impact of these mutations on the activation of oncogenic signalling pathways. Our research provides compelling evidence for the potential functional role of nsSNPs in up-regulating HRAS expression and contributing to the development of various types of cancers.The main objective of our research is identifying and characterising nsSNPs in the HRAS gene and their potential impact on the structure and function of the HRAS protein. Specifically, we explore https://www.ncbi.nlm.nih.gov/snp/), ENSEMBLE (https://ensemblgenomes.org/), SNP500 cancer (https://pubmed.ncbi.nlm.nih.gov/), GeneCards (https://www.genecards.org/), and UniPort (https://www.uniprot.org/), which are periodically refreshed with new information. In particular, the ENSEMBLE repository was availed to obtain the nucleotide and protein sequences germane to the HRAS gene as per the methods of earlier researchers (The ensuing discourse delineates the origins and manipulation of Single Nucleotide Polymorphism (SNP) data garnered from sundry databases like dbSNP (earchers . This unearchers .https://sift.bii.a-star.edu.sg/) was employed to forecast the impact of non-synonymous SNPs on the mutant protein (The SIFT tool ( protein . This te protein . Precise protein . The imp protein .http://genetics.bwh.harvard.edu/pph2/) was leveraged to predict the pernicious ramifications of nsSNPs on the structural and functional aspects of proteins. This prediction was based on the naive Bayesian algorithm, which involves classifying scores from 0 to 1. Mutations were partitioned into three categories, depending on the scores, with those possessing a score closest to 1 being identified as probably damaging and exhibiting a significant impact on protein structure. Implementing this tool could potentially open up research opportunities in the realm of genetic variability and its influence on protein conformation and function.The PolyPhen2 tool (https://snps-and-go/), PhD-SNP (https://snps.biofold.org/), PROVEAN (https://bio.tools/provean), PANTHER (http://www.pantherdb.org/), and P-Mut (https://bio.tools/pmut). In particular, PhD-SNP relied on support vector systems to classify and depict the effects of non-synonymous SNPs on proteins. This method divided nsSNPs into deleterious or neutral categories  tool box was implemented to foretell the impact of nsSNPs on the coding region of the HRAS protein as per earlier researchers (https://switchlab.org/software/), amyloid propensity through WALTZ (https://switchlab.org/software/), and chaperone binding with LIMBO as per investigation of the earlier researcher (The SNP SnpEff and SnpSift (earchers . Apart fearchers . The SNPsearcher . These shttps://bio.tools/i-mutant_suite) was utilized to prognosticate the impact of mutations on the stability of the HRAS protein, with the MUpro program (https://mupro.proteomics.ics.uci.edu/) being employed to validate the outcomes (http://sride.enzim.hu/) was leveraged to pinpoint the stabilizing residues of the native and mutant proteins (The I-MUTANT 3.0 suit (outcomes . Both seoutcomes . Furtherproteins . The inthttps://consurf.tau.ac.il/consurf_index.php) was deployed to thoroughly analyze each amino acid’s conservation and evolutionary aspects in the HRAS protein (The ConSurf server ( protein . The out protein .https://www.chemcomp.com/Products.htm) as per the method of the earlier researcher  as per the analysis of  algorithm to predict the impact of non-synonymous SNPs (nsSNPs) on the structure of the HRAS protein . The SIFWe noted a high frequency of substitutions involving cytosine and thymine or guanine and adenine, while substitutions involving adenine and thymine or adenine and cytosine were rare . This inWe describe the results of further analysis of 50 selected nsSNPs submitted to a server and utilized the PolyPhen2 algorithm to predict the impact of nsSNPs on protein structure. The algorithm provides a score between 0 and 1, with a higher score indicating a greater likelihood of deleterious consequences. Out of the 50 nsSNPs submitted, only 10 had a probabilistic score greater than 0.97, indicating that they were probably damaging nsSNPs. The total of 10 nsSNPs had scores higher than 0.83, which were classified as possibly damaging. We exactly identified seven nsSNPs  with a maximum score of 1, indicating a high probability of being damaging. Most of the remaining mutations had scores in the range of 0.98 to −0.99. We also compared the results obtained from SIFT and PolyPhen2 and observed that ten nsSNPs were identified as common between the SIFT and PolyPhen2 analyses, despite using different methods to obtain results. The SIFT algorithm utilizes structural detail to yield results, while PolyPhen2 is based on structure and has shown a good correlation with the SIFT approach. Finally, we observed that most nsSNPs had a SIFT tolerance index of 0.00, indicating that they were highly intolerant to variation, while their PolyPhen2 scores were >0.90 and based on these findings, we concluded that these alterations may be responsible for disease.The SNP-effect tool assesses the potential effects of genetic variants on protein structure and function. We also evaluate the phenotypic impact of alterations within the HRAS molecule. Specifically, the approach evaluated chaperone binding propensity, aggregation propensity, and amyloid tendency. Chaperones are a class of proteins that assist in properly folding and assembly of other proteins. A protein’s chaperone binding propensity can provide insight into its stability and folding efficiency. Aggregation propensity refers to the tendency of proteins to form aggregates or clumps, which can interfere with proper cellular function. The amyloid tendency measures a protein’s ability to form amyloid fibrils, which are associated with several diseases, including Alzheimer’s and Parkinson’s and Cancer. The results of the SNP-effect analysis showed that the selected mutations had little to no impact on chaperone binding propensity, aggregation propensity, or amyloid tendency. However, it is essential to note that this approach only evaluates a limited set of characteristics and does not comprehensively assess all possible effects. The findings suggest that the selected mutations may have other potential impact on the structure and properties of the HRAS protein, which could have significant implications for biological organisms. Further experimental studies would be necessary to fully understand the impact of these mutations and their potential role in disease. Screening of most deleterious SNPs using different software’s are mentioned in Screening potentially deleterious SNPs in the HRAS gene is essential to understanding the possible impact of genetic variations on the protein’s function . SeveralThe SNP-effect tool is a computational pipeline that assesses the effect of SNPs on protein properties, such as chaperone binding propensity, aggregation propensity, and amyloid tendency . We founhttps://folding.biofold.org/i-mutant/i-mutant2.0.html) server predicted that most modifications can decrease the stability of the HRAS protein, with DDG values ranging from −3.21 kcal/mol to 0.87 kcal/mol. The most significant effect was observed with the I46T mutation, which had a DDG value of −3.21 kcal/mol. Only three mutations, Y4C, T58I, and Y12E, showed perfection in the structural stability of the protein. The MUpro server (https://mupro.proteomics.ics.uci.edu/) provided similar results, except for the Y4C, G12E, and T58I mutations, which showed a decrease in stability. The G15D mutation showed an increase in strength in the MUpro prediction, while I-MUTANT predicted a reduction in stability and the results are summarized in The stability of a protein is crucial for its proper function, and destabilizing mutations can lead to the misfolding and aggregation of the protein, resulting in various diseases . We explThe differences in predictions between the two servers can be attributed to their different calculation methods. I-MUTANT uses a support vector regression algorithm based on various structural properties of the protein, such as solvent accessibility and secondary structure, to predict the DDG values. In contrast, MUpro employs a neural network-based approach that incorporates sequence and structural information, as well as evolutionary conservation, to predict the effects of mutations on protein stability. The results of this study indicate that several mutations within the HRAS gene can destabilise the protein, potentially resulting in disease. Identifying these destabilizing mutations can provide insights into the molecular mechanisms of HRAS-associated diseases and may aid in developing new therapeutic strategies.Our results indicated that most of the mutations decreased the stability of the HRAS protein, while only a few improved it. The mutation with the most significant effect on stability was I46T, with a DDG value of −3.21 kcal/mol. The I-MUTANT server predicted that 15 mutations, including Y4C, G12E, G13R, G15D, D38H, I46T, T58I, A59L, G75R, P34S, G60D, G60V, G115R, R123G, and R123P, decreased the stability of the protein, while Y4C, T58I, and Y12E improved it. The MUpro server provided similar results, except for Y4C, G12E, and T58I mutations, which showed decreasedd stability, contrary to the I-MUTANT prediction. Additionally, MUpro predicted I-MUTANT predicted a decrease inincreased stability for the G15D mutation, and I-MUTANT predicted decreased stability. The DDG values of most mutations ranged from −3.21 kcal/mol to 0.87 kcal/mol, indicating reduced protein stability with a DDG value lowers than 0. The results suggest that mutations can significantly affect the HRAS protein’s stability, which may impact the biological organism. Further research may be required to understand the specific effects of each mutation on HRAS protein stability and its overall impact on biological systems.https://consurf.tau.ac.il/consurf_index.php) is a valuable tool for determining the evolutionary conservation of protein residues across a set of homologous sequences. The conservation scores of HRAS protein residues were analyzed using ConSurf to evaluate the impact of the 10 deleterious mutations on the protein structure and function. Out of the 10 deleterious mutations, 6 missense mutations  were located in highly conserved regions (7–8–9). This finding suggests that mutations in these regions could significantly affect the function and structure of the HRAS protein. Additionally, G13R, D38H, and I46T were predicted to be exposed, while mutants such as A59L, G60V, G60D, Q60D, R123P, and R123G were expected to be functional and revealed mutations.The ConSurf server  and one in an average part, indicating that these mutations may have a milder effect on the protein structure and function. These findings suggest that highly conserved regions of HRAS protein are more sensitive to mutations that could impact the protein function. These results provide insights into the functional and structural effects of the 10 selected deleterious mutations on the HRAS protein. By identifying the regions that are highly conserved and sensitive to mutations, this study can help researchers better understand the consequences of HRAS mutations and may lead to new treatments for diseases associated with HRAS mutations.https://www.rcsb.org/) provides an extensive collection of experimentally determined protein structures that can be used for structural analysis  was used to predict the mutated form using homology modelling approaches. The ten most deleterious mutations, indicated by all the analyzing tools, were mapped in their respective region of HRAS using mutagenesis techniques in Pymol software, as shown in The native complete structure of HRAS was retrieved from the Protein Data Bank, and the Swiss Model server , rs730880460 (G60D), rs730880464 (R123P), rs750680771 (D38H), rs1564789700 (I46T), rs917210997 (G115R), rs369106578 (R123G), rs1204223913 (P11OL), rs727504747 (A59L), and rs104894228 (G13R), were generated using Pymol software and compared with the native HRAS protein structure. The mutations were observed to cause structural changes in different regions of HRAS, and the energy minimization process helped achieve more stable configurations. The results suggest that these mutations could potentially affect the function of HRAS and contribute to cancer development.Each mutation can have different effects on the structure and function of the protein. The specific effects of each mutation can depend on a variety of factors, such as the location of the mutation in the protein, the surrounding amino acid residues, and the protein’s function as per the investigation of the earlier researcher . For exaSimilarly, the G115R mutation, also predicted to be deleterious, can lead to an incorrect conformation and disturbance of the local structure of the protein due to the larger size of the mutant residue, as a result, compare with , and thihttps://www.chemcomp.com/Products.htm), and the docked complexes of the wild-typeand mutated protein with ligands were generated. The docked complexes were analyzed for their docked score, hydrogen bonds, and pi-interactions within the 4.5 Å. The active binding site residues GLU62, GLY10, THR58, ASP33, ILE36, GLU63, TYR64, ALA11, TYR96, GLY13, and LYS16 were identified to be involved in pi-interactions and generate hydrogen bonds. The docked complexes showed significant binding affinity and interactions with the active binding site residues.Molecular docking is a computational method used to predict the binding mode and affinity of small molecules to a protein as per earlier researchers . In thisWhen the three deleterious mutations, G60V, G60D, and D38H, were docked into the same binding pocket, the D38H residue was found to be involved in the binding interactions. With G60V, three hydrogen bonds were generated by the D38H residue, while one hydrogen bond with D38H and two with G60D were observed. This suggests that the D38H residue plays an important role in stabilizing the conformation of the mutated HRAS protein and results were compared with the investigation of the earlier researcher . The docThe -protein residues are shown as sticks, and the ligands are shown in space-filling mode. The wild-typel protein and the three mutated proteins are shown in different colors. The two-dimensional plot of interacting target residues shows the residues of the protein that areprotein residues involved in important interactions with the ligands. The residues involved in hydrogen bonding, Pi, and hydrophobic interactions are shown as circles in different colors. The Non-mutant protein is shown in blue color, and the residues involved in interactions with the ligands are labelled with their residue numbers. The plot in The 3D interaction study of the docked complexes of G60V, G60D, and D38H with Non-mutant protein was performed to visualize the interactions and understand the binding mechanism of the mutated proteins with the Non-mutant protein as shown in In the case of G60D, the mutated residue interacted with residues ALA11, GLY10, and TYR96. The ALA11 residue, involved in Pi-interactions and hydrogen bonding in the Non-mutant protein, formed new interactions with the G60D mutated residue. Similarly, GLY10 and TYR96 residues also included new interactions with the G60D mutated residue. This indicates that the G60D mutation might have altered the binding pattern of the HRAS protein with ligands. In the case of D38H, the mutated residue interacted with residues GLU62, GLY10, and TYR64. The GLU62 residue, involved in Pi-interactions and hydrogen bonding in the Non-mutant protein, formed new interactions with the D38H mutated residue.Similarly, GLY10 and TYR64 residues also formed new interactions with the D38H mutated residue. This suggests that the D38H mutation might have affected the ligands’ binding in the HRAS protein’s active site. Overall, the 3D interaction study revealed that the mutated residues interacted with different residues compared to the Non-mutant protein, which could affect the protein-ligand interactions and the function of the HRAS protein.The RMSD analysis is an essential tool for understanding the structural stability of a protein-ligand complex over a given time interval. The present study focused on analyzing the root-mean-square deviation (RMSD) values of protein-ligand complexes of HRAS in both APO and docked states, to examine the impact of three mutations, namely, G60V, G60D, and D38H, on the stability of the complexes. The findings of this study revealed that the wild-type HRAS complex demonstrated a greater degree of fluctuation compared to both the APO and mutated complexes. These results provide valuable insights into the effects of specific mutations on the stability of protein-ligand complexes and contribute to a better understanding of the dynamics of HRAS proteins in different states. The mean RMSD value of the wild-typecomplex was 4.40 Å, indicating that the protein underwent secondary structure changes with high loop regions compared to the average RMSD values of the mutated models. The mutated models  decreased mean square calculation upon mutation, indicating a more stable environment and compactness of the whole system. The RMSD of the apoprotein was the most durable among all the protein models , with a The results suggest that the mutations in the HRAS protein can lead to a more stable protein-ligand complex structure. The RMSD analysis also revealed that the ligand remained fixed throughout the simulation time interval, indicating that the mutations did not cause any significant changes in the ligand’s position or displacement. The RMSD analysis supports the conclusion that the mutations have a stabilizing effect on the protein-ligand complex. The minor fluctuations observed in the wild-type complex may indicate a higher degree of flexibility, potentially leading to changes in the protein-ligand complex’s structural framework.The RMSF plot was generated to understand the fluctuation and stability of protein residues over the simulation period of 100 ns. The RMSF graph showed that the residues of the normal HRAS complex exhibited higher instability in comparison to the mutated and APO models, indicating more flexibility and conformational changes in the normal complex. The residues that showed higher RMSF values in the wild-type HRAS complex include Ser157, G12E, ILE46, and ASP47, which experienced significant structural changes concerning APO protein. On the other hand, the mutated complexes  showed decreased RMSF values compared to the wild-type HRAS complex, indicating higher stability and less conformational changes, which are shown in Notably, the residues ASN26 and ASP33 of the mutated complexes showed higher RMSF values than the wild-typw HRAS complex, indicating more fluctuations and conformational changes. These residues also showed significant structural changes during the simulation time, which may affect the stability and binding affinity of the mutated HRAS complex with ligands. The RMSF plot indicated that the mutated HRAS complexes were more stable than the wild-type complex, as evidenced by the lower RMSF values. However, some residues still experienced fluctuations and conformational changes, which could affect the stability and functionality of the protein-ligand complex.The RMSF analysis provides information about the flexibility and fluctuation of each residue in the protein structure. The RMSF analysis revealed that the fluctuations occurred at the residual level, leading to the s system stability. The RMSF values showed changes occurring in regions other than the active site, which resulted in relatively more significant fluctuations. The loops in the protein structure are flexible regions, and upon ligand binding during MD simulations, they start changing their configuration at different intervals. The fluctuations in the loops and other flexible regions can be observed in the RMSF analysis. It can provide valuable information about the changes in the protein structure that occur during the simulation.The radius of gyration measures the compactness of the protein structure . It provThe beta (B)-factor, also known as the temperature factor, measures a protein’s thermal stability and flexibility as per earlier researcher . It is cThe study used the B-factor and RMSF to investigate the thermal stability and flexibility of different HRAS models, including the APO (no ligand bound), wild-type, and mutant  HRAS models. The average B-factor values were determined for each model, and the RMSF results were compared with the B-factor data. The results showed that the average B-factor values were highest for the G60D and wild-type HRAS models, indicating that these proteins had greater mobility and lower stability as mentioned in The RMSF results also showed that the thermal instability was higher at specific residues in the different HRAS models. These results were consistent with the B-factor data, as shown in Hydrogen bonds are weak interactions between a hydrogen atom bonded to an electronegative atom (such as oxygen or nitrogen) and another electronegative atom in a nearby molecule . In biolThe results showed that the average number of hydrogen bonds was highest for the D38H mutant (138.9) and lowest for the G60V mutant (127.5), with the APO protein (133.6) and G60D mutant (136) having intermediate values. This suggests that the mutations can affect the number of hydrogen bonds formed within the active site of the HRAS protein. The hydrogen bond analysis also revealed that the number of hydrogen bonds decreased upon the D38H mutation and increased significantly upon the G60V, G60D, and D38H mutations. This suggests that the modifications can affect the hydrogen bonding patterns within the active site of the HRAS protein and potentially alter its structure and function. In molecular docking and drug design studies, it is essential to estimate a ligand’s binding affinity to its target protein. One commonly used method for calculating binding energies is the molecular mechanics/generalized born surface area (MM-GBSA) approach . This meIn addition to examining the RMSD values, this study delved deeper into the binding energies of the protein-ligand complexes by scrutinizing their components. This comprehensive analysis facilitated a meticulous investigation of the specific interactions that play a role in determining the overall binding affinity, including van der Waals forces, electrostatic interactions, and hydrogen bonding. The outcomes of this investigation furnished valuable insights into the fundamental nature of the protein-ligand interactions, unravelling the intricacies of the underlying mechanisms that govern the binding affinity of these complexes. These findings have far-reaching implications for developing novel therapeutics targeting HRAS and allied proteins. An enhanced understanding of the binding energies can optimize drug efficacy and specificity and pave the way for developing more potent and targeted treatments. The results showed that the favourable Van der Waals and Coulombic interactions were the main contributors to the high binding energies in all four systems. Van der Waals interactions are attractive forces between atoms and molecules nearby, while Coulombic interactions are electrostatic forces between charged particles. The study also noted that Coulombic interactions reduced the binding affinity of some ligands towards the active site residues. This is because electrostatic repulsion between negatively charged ligands and negatively charged residues in the active site can decrease the strength of the binding. MM-GBSA calculations performed in this study provided insights into the binding energies and components of the G60V, G60D, and D38H mutants, as well as the normal HRAS protein.The outcomes manifest that all four systems exhibit highly favourable binding energies, with the mutants displaying slightly higher binding energies than the normal HRAS protein. This suggests the mutations could intensify ligands’ binding to the protein’s active site. The Coulomb solute and Coulomb solvent energies were identified to be the highest in all four systems, indicating that electrostatic interactions play a significant role in the binding of ligands to the protein. The VDW solute and VDW solvent energies were also substantial, with the VDW solvent energy being the highest for the normal HRAS protein. This suggests that the solvent molecules have a part in stabilizing the protein-ligand complex. Regarding the particular mutants, the G60D mutant was observed to have the highest Coulomb solvent energy, while the D38H mutant had the lowest Coulomb solute and VDW solute energies. This implies that the mutations may affect the electrostatic and van der Waals interactions between the protein and solvent molecules, potentially influencing the binding of ligands to the active site.The ΔG binding free energies of all the systems, including the wild-typesystem, were determined to be quite favourable, ranging from −4,902.33 to −5,068.6 kcal/mol, indicating that the binding of ligands to the HRAS protein is energetically stable. The favourable binding energies were due to the highly favourable Coulombic and Van der Waals interactions between the ligands and the protein. The Coulombic interactions between the ligands and the protein were more favourable in all the systems, resulting in lower binding energies. The electrostatic solvation energy of Generalized Born was compensated by the Coulombic interactions, which were less favourable. This suggests that the Coulombic interactions play a significant role in ligand binding towards the active site residues in the HRAS protein.in vitro findings logically. Overall, the MM-GBSA calculations provide valuable insights into the thermodynamic stability of the ligand-protein complex, which is crucial for understanding the binding affinity and specificity of the ligand towards the protein. The favourable binding energies and strong Coulombic interactions observed in this study suggest that the ligands are likely to bind tightly to the active site residues of the HRAS protein, which may have implications for the design of novel HRAS inhibitors with improved efficacy and specificity.All four complexes displayed a clear pattern, which was considerably similar, indicating that the mutations did not significantly affect the binding energy. This observation is intriguing and prompts further investigation into the conformational characteristics of the systems, i.e., the interaction between the ligands and the receptor. The molecular docking and dynamics results may explain the The study aimed to investigate the impact of mutations on the structure and stability of the HRAS protein. To achieve this, the native structure of the HRAS protein was obtained from the Protein Data Bank (PDB), and homology modeling approaches were used to predict the mutated structure of the protein. The mutations were identified using various Single Nucleotide Polymorphism (SNP) databases, including rs730880460 (G60V), rs730880460 (G60D), rs730880464 (R123P), rs750680771 (D38H), rs1564789700 (I46T), rs917210997 (G117R), rs369106578 (R123G), rs1204223913 (P11OL), rs727504747 (A59L), rs104894228 (G13R), and rs1564789552 (Y64H). The PyMOL software was utilized to generate the mutated models of HRAS.The energy minimization process was carried out using Schrödinger, a method that minimizes the energy and force load applied upon every atom in a gathering of atoms to obtain the best thermodynamically stable HRAS structure. The final and stable model of the HRAS protein was obtained after energy minimization, and the energy value was reported to be −17,755 kJ/mol, significantly lower than the initial energy value of −107916 kJ/mol. This indicates that the energy minimization process significantly improved the stability of the protein structure. The results of this study suggest that mutations can dramatically alter the structure and stability of the HRAS protein. The homology modelling approach successfully predicted the mutated models of the protein, and the energy minimization process further improved the stability of the predicted structures. The energy minimization process revealed a remarkable reduction in the energy value of the predicted protein structures. This significant decrease in energy strongly suggests that the systems are thermodynamically stable and likely to represent the native conformation of the protein. The observed reduction in energy value serves as a reliable indicator of the stability and accuracy of the predicted structures, bolstering confidence in the findings of this study.The study is significant in providing insights into the impact of mutations on the stability of the HRAS protein. The mutated models generated in this study can be used for further studies to understand the effect of mutations on protein function and interactions. Overall, the results of this study provide a foundation for developing new HRAS inhibitors that can target the mutated forms of the protein with improved efficacy and specificity.In the case of R123P, substituting R with P at position 123 may result in a significant loss of interaction due to the marked disparity in size and hydrophobicity between the wild type and mutant residues. The smaller size of the mutant residue may significantly impede the protein’s interactions with other molecules. In D38H, the mutation of D to H at position 38 can potentially disturb the protein’s interaction with other parts of the protein. The loss of the charge of the wild type residue due to this mutation may significantly impact the protein’s functionality. In I46T, substituting I with T at position 46 can cause the core region of the protein to lose its hydrophobic interactions. This is because the mutant residue is, more minor, but more hydrophobic than thewild-type residue. In G117R, the mutation of G to R at position 117 could result in a protein folding problem. Introducing a charge in a buried region of the protein due to the mutant residue, which is normally flexible, may disrupt the local structure of the protein. In R123G, the mutation of R to G at position 123 could cause the protein to lose its ability to interact with other molecules due to the charge difference among mutant and wild-type res and. In G60V, substituting P with L at position 110 can result in the loss of external interactions with other proteins in a pathway due to the difference in size between the mutant and wild-type residues. The native residue is embedded in the core region, which may make it difficult for the more considerable mutant residue to fit. In A59L, the mutation of A to L at position 59 can potentially cause the mutant residue, which is larger than the wild-type residue, to not fit in the protein’s core region. This could result in a loss of external interactions. In G13R, substituting G with R at position 13 may significantly impede the protein’s interaction with other molecules. Introducing a charge due to the mutant residue may cause a repelling effect between the mutant and adjacent residues, which are typically found on the protein’s surface. In Y64H, converting Y to H at position 64 may result in a loss of external interactions at this point has potential result in the loss of hydrophobic interactions with other molecules.in silico and experimental. Experimental approaches involve biochemical and biophysical techniques, such as X-ray crystallography, NMR spectroscopy, and site-directed mutagenesis, to directly measure the functional impact of nsSNPs. However, these methods are time-consuming, expensive, and require specialized expertise. In contrast, in silico approaches use computational methods to predict the functional impact of nsSNPs, making them a faster and more cost-effective way to screen for potentially deleterious variants.Single nucleotide polymorphisms (SNPs) are a type of genetic variation in the human genome that can result in amino acid substitutions, leading to alterations in protein structure and function . Non-synin silico tools for predicting the functional effects of nsSNPs. SIFT uses a sequence homology-based approach to predict the impact of an amino acid substitution on protein function. It compares the protein sequence of interest to a database of related protein sequences and determines the degree of conservation of the substituted amino acid. If the amino acid is highly conserved, it is predicted to be intolerant to substitution, and the variant is classified as damaging. PolyPhen-2, conversely, uses a combination of sequence- and structure-based approaches to predict the impact of amino acid substitutions on protein structure and function. It considers factors such as the physicochemical properties of the substituted amino acid, the local protein structure, and the conservation of the substituted amino acid across different species. Variants are classified as damaging if they are predicted to affect protein function, stability, or interaction with other molecules. Both SIFT and PolyPhen-2 have been shown to have high accuracy in predicting the functional impact of nsSNPs. However, they use different algorithms and approaches, which can lead to differences in their predictions. Therefore, it is recommended to use both tools to increase the accuracy of predictions. In addition, other tools, such as PROVEAN, MutPred, and MutPred-LOF, are also available and can be used in combination to improve the accuracy of predictions.SIFT (Sorting Intolerant from Tolerant) and PolyPhen-2 (Polymorphism Phenotyping v2) are widely used in silico tools to identify potentially deleterious SNPs that may be associated with the disease. After searching the SNP databases, 50 hits were found, and the mutants’ rsIDs were submitted to SIFT and PolyPhen-2 for functional analysis. Of the 50 SNPs, 24 were found to be non-tolerable by SIFT, and 28 were predicted to be possible or probably damaging by PolyPhen-2. The results were then validated using other tools, and 11 SNPs were identified as deleterious by all of the tools. The impact of these deleterious SNPs on protein structure was analyzed using software such as PyMOL and SNP effect. In conclusion, in silico approaches and tools have become powerful tools for identifying and characterizing the functional effects of nsSNPs and predicting their potential association with various diseases. While no single tool can accurately predict the practical effects of nsSNPs, a combination of tools can provide more accurate and reliable predictions. Moreover, detailed analysis of the impact of deleterious SNPs on protein structure and function can provide important insights into the development of genetic disorders and may lead to the development of novel therapeutic approaches.Other tools, such as SNP&GO, P-Mut, Phd SNP, and ROVEAN, have also been developed to predict the functional effects of nsSNPs. These tools use different algorithms and approaches to predict the impact of nsSNPs on protein structure and function. They can provide complementary information to other tools. In recent research, a comprehensive analysis of nsSNPs in the HRAS gene was performed using various Bioinformatics is an essential tool for analysing genetic variations, including non-synonymous single nucleotide polymorphisms (nsSNPs) . In-siliIn a recent study by The authors emphasized that combining multiple algorithms frequently is a powerful tool for selecting candidate functional nsSNPs. In a previous study by In our study, we utilized SNP effect to assess the impact of SNPs on the aggregation tendency, amyloid propensity, and chaperone binding of HRAS protein. The outcomes of SNP effect revealed that rs1204223913 increases the aggregation propensity of HRAS protein with a dTANGO score of 547.61, while rs1564789700 decreases the aggregation propensity with a dWALTZ score of −106.36. However, we observed that most of the variations did not affect the molecular phenotype of the protein. Although these variations may convey some damaging mutation on HRAS protein, they seem unrelated to the protein’s aggregation tendency, amyloid propensity, or chaperone binding tendency, according to the outcomes of the SNP effect. To further investigate the impact of deleterious nsSNPs on the protein structure of HRAS, we retrieved the tertiary structure of HRAS from PDB with PDB id (6MQT). We mapped all 10 deleterious nsSNPs using PyMOL software. Our findings indicate that these nsSNPs are located in different regions of the protein structure and may affect the protein function by altering its stability or interactions with other molecules.in silico tools to predict the effects of nsSNPs on protein function and reported that these tools aid in identifying deleterious nsSNPs associated with human diseases. Additionally, a study by in silico tools can provide a more accurate prediction of the functional impact of nsSNPs on proteins.Several previous studies have also investigated the impact of nsSNPs on protein structure and function. For instance, a survey by Using computational methods, our investigation aimed to detect harmful single nucleotide polymorphisms (SNPs) in the HRAS gene. By examining several SNP databases and employing different analytical tools, we identified 10 damaging SNPs. Our study is significant because mutations in HRAS are associated with various diseases, including cancer. Recognizing potentially harmful SNPs in HRAS can help us understand the genetic basis of these diseases and facilitate the development of personalized treatments. Moreover, using computational methods to identify functional nsSNPs can considerably reduce the time and cost required for experimental validation. Our findings emphasize combining various algorithms and tools to pinpoint candidate functional nsSNPs. In conclusion, our research offers a valuable contribution to the genetics field and can assist in developing personalized medical interventions.in silico approaches, including SIFT analysis, PolyPhen2 scores, and TI scores, to predict the potential impact of rare genetic variants on HRAS protein function, and identified 50 nsSNPs in total, of which 23 were located in the exon region of the HRAS gene and were likely to be deleterious. Among these 23 nsSNPs, 10 had the most destructive impacts, including G60V, G60D, R123P, D38H, I46T, G115R, R123G, P11OL, A59L, and G13R, with DDG values ranging from −3.21 to 0.87 kcal/mol. We conducted molecular dynamics (MD) simulations to analyze the stability, flexibility, and compaction of the HRAS protein to investigate the consequences of specific non-synonymous single nucleotide polymorphisms (nsSNPs). Our predicted results indicated that the stable model of HRAS had a significantly lower energy value than the initial model, suggesting that these nsSNPs may alter the stability of the protein. Furthermore, we analyzed the binding energies of both the wild-type and mutant HRAS protein with docked complexes to understand the potential impact of these mutations on the activation of oncogenic signalling pathways. Our findings indicated that the G60V, G60D, and D38H mutants had higher binding energies than the wild-type HRAS protein, potentially leading to the activation of oncogenic signalling pathways and contributing to the development of various types of cancers. Our systematic study analysis provides essential insights into the potential functional role of nsSNPs in the HRAS gene in cancer development. It could inform future studies aimed at developing targeted therapies for cancer treatment.We investigate the potential impact of nsSNPs in the HRAS gene on the structure and function of the HRAS protein, as well as their potential contribution to the development of various types of cancers. Specifically to identify and characterize nsSNPs within the coding region of HRAS that can cause detrimental mutations, disrupt normal protein function, and activate oncogenic signaling pathways. To decrease expenses and enhance the efficiency of genetic association studies, We applied