Quantitative Structure-activity Relationship Studies Research Articles

QSAR STUDIES ON HUMAN CARBONIC ANHYDRASE II INHIBITORS

Carbonic anhydrase II is one of the forms of human α carbonic anhydrases which are ubiquitous metalloenzymes that catalyze inter-conversion of carbon dioxide and water to bicarbonate and proton, overexpression of which leads to disorders such as glaucoma. 2D and 3D Quantitative Structure Activity Relationship studies were carried out on previously synthesized series of sulfanilamide derivatives by VLife MDS software using stepwise variable, multi-linear regression and k-nearest neighbor molecular field analysis methods. 2D-QSAR model depicts contribution of halogens (such as chlorine and fluorine), methylene and oxygen atoms to inhibition of human carbonic anhydrases II activity. Using k-nearest neighbor molecular field analysis method two 3D-QSAR models (model A and B) were generated from which model A was found to be the best validated model with q2 (0.9494), pred_r2 (0.7367) and q2 _ se (0.2037). It displayed the fact that the inhibitory action of sulfanilamide derivatives against human carbonic anhydrases II is influenced by hydrophobicity and electro positivity.

Structure-based simulated scanning of rheumatoid arthritis inhibitors: 2D-QSAR, 3D-QSAR, docking, molecular dynamics simulation, and lipophilicity indices calculation

Rheumatoid arthritis (RA) is an autoimmune condition in the world, affecting about 1% of the population. It is characterized by a cartilage attack unique to the tissue in the peripheral joints. Different types of drugs are used to treat the disease are aspirin, ibuprofen, naproxen and celecoxib, but their use is less effective due to increased drug resistance and the side effects of these drugs. The need for new anti-rheumatoid arthritis drugs with improved activities and a new mechanism of action along diverse mechanisms is therefore necessary. In this study, 30 compounds from PubChem database with accession number 435,024 were carefully chosen as innovative RA inhibitors to battle autoimmune deficiency and were laid open to quantitative structure-activity relationship (QSAR), comparative molecular field analysis (CoMFA), molecular docking, molecular dynamic simulations, ADMET, Frontier Molecular Orbitals, and golden triangle studies. In the QSAR study, multiple linear regression was used for descriptor selection. The results showed that QSAR (Ntraining = 18; R2 = 0.796; R2adjusted = 0.7523; F = 18.209; Q2 = 0.67999) take good stability and predictability. In the CoMFA fractional factorial design (FFD) and CoMFA smart region definition (SRD) study, based on the Open3DALIGN-based arrangement used, results have shown that the CoMFA (FFD) model with reliable predictive ability was chosen as the final model to perform modeling study. The CoMFA (FFD) model, which shows comparatively good execution, was applied to search the necessary structural areas where the adjustment was essential to design a novel compound with bettered bioactivity. The molecular docking study showed that compound 4 has the least binding energy of -8.0 kcal/mol using PyRx software and compound 17 has the least binding energy of -8.55 kcal/mol using AutoDock4.2 software, respectively. Based on docking results on compounds 4 and 17 have good predictive behavior and relatively good ADMET exposure. MD simulation analysis of 1000 ps shows that one hydrogen bond with the residues was formed by compound 17 (Lys11) and binds distantly to the protein. Compound 4 has a smaller energy gap (-6.5 eV) have great penetrating power, optimized clearance and oral absorption compared to compound 17. These studies offer valuable insights into the discovery and design of RA inhibitors in a modern age.

QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823.

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) statistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

2D‐QSAR and molecular docking studies of carbamate derivatives to discover novel potent anti‐butyrylcholinesterase agents for Alzheimer's disease treatment

AbstractCurrently, anti‐butyrylcholinesterase (anti‐BuChE)is among the greatest therapeutic agents for the treatment of Alzheimer's disease. In this research, a series of 36 carbamate derivatives were subjected to a quantitative structure–activity relationships study using DFT and Lipinski's descriptors. Multiple linear regression (MLR) was used to explore the relationships between the structural features of these compounds and BuChE inhibitory activity. In order to generate results applicable in the experimental plan, the Organization of Economic Cooperation and Development guidelines were adopted. The quality of MLR model was evaluated by several statistical parameters including internal and external validation parameters (R2, R2adj, F, VIF, R2test, Q2CV, R2Rand, Q2CV [Rand], and cRp2). The built model displayed a high predictive power (R2test = 0.817). What is more, the internal validation parameters (Q2CV = 0.774; average R2Rand = 0.118; average Q2CV [Rand] = −0.438; cRp2 = 0.820) highlight the robustness of our built model. Besides, the obtained result revealed that anti‐BuChE activity is mainly attributed to the following molecular descriptors: octanol–water partition (log P), highest occupied molecular orbital energy (EHOMO), total energy (ET), and dipole moment (μ). Based on these findings, a series of newer synthesizable compounds with enhanced anti‐BuChE activities were designed and their ADMET and drug‐likeness properties were further predicted to filter out compounds likely to fail during drug development stages. Finally, molecular docking and molecular dynamics were performed to identify the binding types between the best designed compounds and BuChE enzyme.

Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test

Abstract Quantitative structure–activity relationship (QSAR) study has been conducted on 36 terpene derivatives with anticonvulsant activity in timed pentylenetetrazole (PTZ) infusion test. QSAR models for anticonvulsant activity prediction of hydrazones and esters of some monocyclic/bicyclic terpenoids were developed using simplex representation of molecular structure (SiRMS; informational field [IF]) approach based on the SiRMS and the IF of molecule. Four 2D partial least squares QSAR consensus models were developed with the coefficient of determination for test sets R test 2 > 0.62 {R}_{\text{test}}^{2}\gt 0.62 . Based on the established QSAR models, we found that carvone and verbenone cores possess the most significant contribution to antiseizure action examined on the model of PTZ-induced convulsions at 3 and 24 h after oral administration of terpene derivatives. Moreover, carbonyl and hydroxy group substitution in terpenoid molecules followed by hydrazones and esters formation leads to enhancement and prolongation of antiseizure action due to the contribution of additional molecular fragments. The presented QSAR models might be utilized to predict anticonvulsant effect among terpene derivatives for their oral administration against onset seizures.

Identifying promising anticancer Sulforaphane derivatives using QSAR, Docking, and ADME studies

In cruciferous vegetables such as broccoli, sulfur-rich isothiocyanates, most notably sulforaphane (SFN), has shown anti-cancer properties, including cell cycle regulation, inducing apoptosis, and metastasis. Sulforaphane is a natural antioxidant that regulates several signal transduction pathways controlling oxidative stress, cellular defense, and cardiovascular disease. This paper attempts to elucidate the most drug-like SFN derivative through computational methods, including molecular docking, Quantitative Structure-Activity Relationship (QSAR), and absorption, distribution, metabolism, and excretion (ADME) analysis. From our studies, we can conclude that phenylethyl isothiocyanate (PEITC) has the most therapeutic potential out of a small set of 7 SFN derivatives. It is a confirmed lead-like compound by testing QSAR descriptors, notably the Dragon consensus drug-like score and lead-like score 2. PEITC also proves to be the most bioavailable derivative, as it is predicted to have high gastrointestinal absorption (GIA) and blood-brain barrier (BBB) permeability. In addition, it is in the optimal range for 5 out of 6 bioavailability properties proposed by the Abbot Bioavailability Score1. Lastly, from docking studies, PEITC had the highest average binding affinity overall, meaning that it holds a vital role in cancer prevention through molecular mechanisms.

Computing Some Eccentricity-Based Topological Indices of Para-Line Graphs of Hexagonal Cactus Chains

Let be a simple molecular graph without directed and multiple edges and without loops. Graph theory has become an important component of the chemical mathematics, and it has become one of the most powerful mathematical tools in the analysis and study of the architecture of a chemical network. It studies of descriptors in quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies in the chemistry science. There are a lot of topological indices in QSPR/QSAR studies. In this paper, some eccentricity-based topological indices namely the eccentric connectivity index the modified eccentric connectivity index the total eccentricity index the second Zagreb eccentricity index and the average eccentricity index are computed for the para-line graphs of some hexagonal cactus chains namely the para-chain the ortho-chain and the meta-chain

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q2 = 0.63, R2 = 0.83 and = 0.70) and the CoMSIA (Q2 = 0.584, R2 = 0.816, and = 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis. Communicated by Ramaswamy H. Sarma

DESIGN, SYNTHESIS AND BUILDING A QSAR MODEL FOR THE INHIBITION OF MYCOBACTERIUM TUBERCULOSIS BY PYRAZOLINE DERIVATIVES

A series of chalcone annulated pyrazoline conjugates have recently been synthesized from our laboratory and identified as possible “LEAD”s against Mycobacterium tuberculosis (MTB) through an in-silico study. The MTB enoyl acyl carrier protein reductase (InhA) plays a vital role in the MTB fatty acid synthesis pathway as essential in the mycolic acid biosynthesis. Therefore, inhibition of InhA could block the biosynthesis of mycolic acids, resulting in mycobacteria death. To further explore the potency of the compounds, a quantitative structure-activity relationship (QSAR) study is carried out. To develop a new pharmacophoric model for this inhibition, a QSAR approach of reported pyrazoline derivatives against tuberculosis has been studied. Multiple linear regression analysis was performed to derive the QSAR models, which were further evaluated internally as well as externally for the prediction of activity. The accurate MIC values (μg/ml) were collected for 23 pyrazoline analogs, and other descriptor parameters, such as log p (o/w), MR, DM, EELE, LUMO, HOMO, and TPSA were compared with these analogs. A training set of 23 analogs, all having a common pyrazoline moiety, provided a cross-validated correlation coefficient (r2) value of 0. 45112 and root mean square error (RMSE) value of 0. 0.27760. Moreover, based on this QSAR study, we have hypothetically estimated the predicted pMIC50 by using trainpred.fit file from Molecular Operating Environment 2009.10 software for the newly synthesized test set i.e., six chalcone based pyrazoline compounds. The resulting QSAR model generated from the present study may be useful in the design of a similar group of pyrazoline analogs as anti-tubercular agents.

A Combined approach of QSAR study, molecular docking and pharmacokinetics prediction of promising Amide-Ac6-aminoacetonitriles Cathepsin K inhibitors

• Combined use of QSAR, molecular docking and pharmacokinetics prediction approaches. • One promising Cathepsin K inhibitor was identified. Cathepsin K (Cat-K) highly expressed in osteoclasts, is considered to be a promising target for osteoclast-related maladies. In the present study, quantitative structure–activity relationships (QSAR) and molecular docking approaches were used to identify promising Cat-K inhibitors from a series of Amide-Ac6-aminoacetonitriles analogs. During the two-dimensional QSAR study, the statistical model PLS_MDL was chosen (r 2 = 0.823, q 2 (cross-validation) = 0.732). Descriptors with positive or negative contributions were derived from model PLS_MDL. In order to build robust three-dimensional QSAR models, three different molecular fingerprints were used as analysis precepts for clustering and three different minimization algorithms were employed for generation of molecular poses. The best database which used molecular fingerprint of FCFP and minimization algorithm of Smart Minimizer was selected for further QSAR model construction. The ideal CoMFA and CoMSIA models were acquired (CoMFA: r 2 = 0.963, q 2 = 0.674, r 2 pred = 0.702 and Q 2 (F2) = 0.616; CoMSIA_SEHA: r 2 = 0.989, q 2 = 0.622, r 2 pred = 0.719 and Q 2 (F2) = 0.638). Physicochemical, structural, electrostatic, and steric properties were derived from the created QSAR models and then applied to portray a design scheme. Furthermore, after a selecting of docking scheme, docking studies were carried out to measure the binding affinity of eligible query compounds and to reveal the detailed interactions between ligand and the target protein. Amongst these designed compounds, compound19OX32 was focused for its preferable predicted activity and docking score. Strong interactions and extra hydrogen bonds which echoed the previous summarized design scheme were also observed via a deep investigation of docking result. Consequently, compound 19OX32 was identified as promising Cat-K inhibitor.

Quantitative and Qualitative Structure Activity Relationship Study for Development of the Pet- Inhibitory Activity of 3-Nitro-2,4,6- Trihydroxy Benzamides

The aim in this paper of these work present two components analyses quantitative and qualitative which consisted in the development and evaluation of the first component quantitative and structure activity relationships (QSAR) for the prediction In fact, various compounds inhibiting photosynthesis constitute the largest class of commercial herbicides. All of those inhibitors, including ureas, triazines, bis carbamates and phenols, interrupt photosynthetic electron transport (PET) by binding to quinine-binding protein (D1-protein). Various physicochemical descriptors were used in multiple linear regressions method (MLR) to develop the theoretical models, than using a cross-validation with leave-one-out method to optimize the model as well as possible to fit with the biological data, the most common approach used to study. The second quality of the compound compared with criteria for their power, these rules Lu Propose the four basic characteristics that Lipinski has identified.

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies.

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

Suggestion of active 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model

ABSTRACT The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski’s rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

Identification of key structural features of phosphate and thiophosphate tricyclic coumarin analogs as STS inhibitors

Steroidal sulfatase (STS) is a group of arylsulfatase C enzymes that are involved in the metabolism of steroids. The increase in the circulation of the steroidogenic hormone Estradiol (E2) is responsible for causing breast cancer. Therefore, inhibitors of STS are proved to be an attractive target in the development of lead molecules against breast malignancy. In this work, Quantitative Structural Activity Relationship (QSAR) studies were performed on a dataset of 72 molecules of tricyclic coumarin analogs using random selection in QSARINS software and the statistical technique Genetic Algorithm coupled Multiple Linear Regression (MLR) was employed with the best model prediction R2 = 0.86 and Q2loo = 0.7913. The inclusion of descriptors Mor15m and Mor18v has developed a well-fitted and highly predictable model. Furthermore, the molecular docking and dynamics simulations helped us identify the binding interactions and estimate the stability of the complexes respectively. The binding analysis of the compounds into human sulfatase protein (PDB code: 1P49) resulted in prominent hydrophobic interactions with Arg98, Val486, Phe488, Gly100, Val101, and Lys368. The top-scoring compound 9o and compound 41 were studied for stability analysis in comparison with the standard Irosustat and the RMSD was found to be 5.4 Å. Based on our findings, we report the inclusion of the necessary structural features of coumarin derivatives leads to the development of potent candidates for further development.

Repurposing benzimidazole and benzothiazole derivatives as potential inhibitors of SARS-CoV-2: DFT, QSAR, molecular docking, molecular dynamics simulation, and in-silico pharmacokinetic and toxicity studies

Density Functional Theory (DFT) and Quantitative Structure-Activity Relationship (QSAR) studies were performed on four benzimidazoles (compounds 1–4) and two benzothiazoles (compounds 5 and 6), previously synthesized by our group. The compounds were also investigated for their binding affinity and interactions with the SARS-CoV-2 Mpro (PDB ID: 6LU7) and the human angiotensin-converting enzyme 2 (ACE2) receptor (PDB ID: 6 M18) using a molecular docking approach. Compounds 1, 2, and 3 were found to bind with equal affinity to both targets. Compound 1 showed the highest predictive docking scores, and was further subjected to molecular dynamics (MD) simulation to explain protein stability, ligand properties, and protein–ligand interactions. All compounds were assessed for their structural, physico-chemical, pharmacokinetic, and toxicological properties. Our results suggest that the investigated compounds are potential new drug leads to target SARS-CoV-2.

Anti-Hepatocellular Carcinoma Biomolecules: Molecular Targets Insights

This report explores the available curative molecules directed against hepatocellular carcinoma (HCC). Limited efficiency as well as other drawbacks of existing molecules led to the search for promising potential alternatives. Understanding of the cell signaling mechanisms propelling carcinogenesis and driven by cell proliferation, invasion, and angiogenesis can offer valuable information for the investigation of efficient treatment strategies. The complexity of the mechanisms behind carcinogenesis inspires researchers to explore the ability of various biomolecules to target specific pathways. Natural components occurring mainly in food and medicinal plants, are considered an essential resource for discovering new and promising therapeutic molecules. Novel biomolecules normally have an advantage in terms of biosafety. They are also widely diverse and often possess potent antioxidant, anti-inflammatory, and anti-cancer properties. Based on quantitative structure–activity relationship studies, biomolecules can be used as templates for chemical modifications that improve efficiency, safety, and bioavailability. In this review, we focus on anti-HCC biomolecules that have their molecular targets partially or completely characterized as well as having anti-cancer molecular mechanisms that are fairly described.

The c-Src kinase inhibitors: 2D-QSAR study by Multiple Linear Regression method

A two-dimensional quantitative structure-activity relationship (2D-QSAR) study was conducted on a series of 34 purine derivatives against c-Src tyrosine kinase. This analysis is affected by two statistical methods integrated with XLSTAT software: principal component analysis (PCA) and multiple linear regression (MLR). The best model established by multiple linear regression (R 2 =0,802, Q 2 cv=0,757, R 2 test =0.696) showed very satisfactory results. The proposed QSAR model was verified by internal and external tests, such as Y randomization test and Golbraikh-Tropsha criteria. The domain of applicability of the MLR model has been investigated using the Williams diagram to detect the compounds that are outside this domain.

Evaluating anti-coronavirus activity of some phosphoramides and their influencing inhibitory factors using molecular docking, DFT, QSAR, and NCI-RDG studies

The recent prevalence of coronavirus disease in 2019 (COVID-19) has triggered widespread global health concerns.Antiviral drugs based on phosphoramides have significant inhibitory activity against the main protease (Mpro) of the virus and prevent transcription and viral replication. Hence, in order to design and introduce a group of inhibitors affecting the coronavirus, 35 phosphoramide compounds based on phospho-guanine and phospho-pyrazine derivatives were selected for molecular docking study. The results showed that most phosphoguanides containing the amino benzimidazole have a high interaction tendency with COVID-19. Among them, compound 19 was identified as the strongest inhibitor with the -9.570 kcal/mol binding energy whereas, the binding energy of Remdesivir is -6.75 kcal/mol. The quantitative structure-activity relationship (QSAR) results demonstrated that the number of aromatic rings, amide's nitrogens and their ability in π-staking, and hydrogen interactions with Mpro active sites are major factors contributing to the inhibitory activity of these compounds.Also, the NCI-RDG and DFT results were in good accordance with those of QSAR and molecular docking. The findings of this investigation can be underlying the synthesis of effective and efficient drugs against COVID-19.

Unprecedented 2D GNR-CoB nanocomposite for detection and degradation of malachite green – A computational prediction of degradation pathway and toxicity

In the present work, we have synthesized a novel 2D GNR-CoB composite and was applied it for electrochemical sensing and photocatalytic degradation of the malachite green (MG). The physicochemical properties of the 2D GNR-CoB were analyzed using X-ray diffraction, Transmission electron microscopy, Energy dispersive X-ray diffraction which depicts the morphological and crystalline nature of the prepared composite. The pencil graphite electrode modified with 2D GNR-CoB composite showed excellent electrochemical response for MG detection with a LOD of 1.92 nM, linear range of 25–350 nM with a high sensitivity of 1.714 μA μM−1 cm−2. Besides, the 2D GNR-CoB modified PGE exhibited good recovery for the detection of MG in real samples such as green peas and lady's fingers. Furthermore, the 2D GNR-CoB modified electrode showed excellent photocatalytic activity for the degradation of MG. It suggests that under visible light, GNR-CoB material generates superoxide (·O2−) and hydroxyl (·OH) radicals for MG degradation. The prepared composite showed an efficiency of 91.28% towards the degradation of MG. Based on the experimental analysis and density functional theory calculations, a photocatalytic degradation mechanism pathway for MG is proposed. A quantitative structure-activity relationship study was used to examine the toxicity of the degradation intermediates.

Study on Umami Characteristics of Proline Dipeptides in Fish Sauce Based on STC-1 Cells Taste Sensing Model

This paper aimed to explore the umami characteristic of proline dipeptides in fish sauce. The STC-1 cells taste sensing model was established based on sensory evaluation in this study. Calcium imaging was used to reflect the taste dose effect of proline dipeptides. In order to identify the main receptors that mediate umami taste of proline dipeptides, effects on the mRNA expression of umami receptors (T1R1, T1R3, CaSR and GPRC6a) that dipeptides caused were measured by real-time quantitative PCR. The results showed that none of the 13 proline dipeptides had bitter taste, and most of them presented umami and sour taste. The dipeptides with stronger taste also had lower threshold values, among which the peptides with excellent taste activity were Ser-Pro, Ala-Pro, Pro-Ala, Pro-Val and Pro-Ile. The proline dipeptides with stronger taste activity had stronger upregulation effect on expression of umami taste receptors mRNA, and T1R1 was likely to be an important receptor mediating the umami taste of proline dipeptides. This study provided a non-artificial sensory evaluation method for evaluating umami taste of proline dipeptides in fish sauce, and laid a foundation for further study of quantitative structure-activity relationship of umami oligopeptides in fish sauce.