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Spirulina platensis contain variety of pigments, such as chlorophylls, carotenoids, and phycocyanin. Phycocyanin (PC) exists in abundance, but due to its instability, the utilization of this pigment is still very limited. In this study, the PC was modified using formaldehyde crosslinks yielding phycocyanin-formaldehyde (PC-F), and its photostability was evaluated. The PC-F formation was designated by the distinctive alterations of the maximum absorption to 611 nm, which was 10 nm blue-shifted than those of the PC. Additionally, the sharp peaks of FTIR spectra at 1636 nm for C O and at 1019 nm for C O C, suggesting the interaction of phycocyanin with formaldehyde. The PC-F showed stabilization improvement up to 1.53-folds after 300 mins of yellow light exposure than those of PC. Contrary to yellow light irradiation, a severe decrease of PC-F absorbance was observed reach to 4.9-folds under UV-B irradiation. The poor stability of PC-F upon white light and UV-A irradiation were indicated by the decline of PC-F absorbance up to 1.72 and 1.80, respectively. Moreover, the present study suggests that the modification of phycocyanin by formaldehyde crosslink can increase photostability upon yellow light irradiation.

Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be used to treat dengue. Use of clinically investigated drugs, such as Balapiravir, is still not effective in inhibiting the activity of virus replication. The design of a drug candidate can be performed by using the non-structural protein 3 (NS3) as target. This study aimed to develop QSAR models to predict the inhibitory activity class of NS3 inhibitors. The classification was performed by using feature importance analysis for selecting the descriptors and three ensemble methods, i.e. random forest (RF), adaptive boosting (AdaBoost), and extremely randomized trees (ERT), for model design and prediction. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that model 9, developed from ERT produced the best performance with values of accuracy and AUC equal to 0.73 and 0.82, respectively. Use of y-scrambling method allowed us to confirm that the model was not related to the chance correlation.

Selecting the best talents from a large number of job applicants is challenging, especially for big companies that usually receive tens of thousands of applicants for every job opening. One of the most costly and time-consuming applicant selection stages is the interview process, since it usually performs face to face meetings and involves third parties to do the interviews and analyze the result. To this end, Human Capital Directorate at Telkom Indonesia adopts AI technology to automate some stages of job applicant selection to reduce manual process and third-party involvement. In this paper, we investigate appropriate feature extraction methods to automate job interview grading for reducing bias and human errors. TFIDF, one of the most popular feature extractions, is compared with word embedding to find the optimal method and parameters in classifying interview verbatims with ANN classifier. Based on the test results, the average accuracy for TFIDF outperforms word embedding by 85.22% against 74.88%, respectively. Therefore, for the case of job interview grading using our dataset, TF-IDF performs better to reduce the number of dimensions.

Malaria is a disease that caused many adverse effects on humans. Various attempts have been done to find new anti-malarial agents due to the resistance problem of the existing drug. Fusidic acid is known as one of a compound that is promising to be used as an anti-malaria agent. However, this compound should be derived to obtain a new fusidic acid derivative that has better activity. The exploration of the compound in conventional style has a shortcoming in the term of time and cost. Therefore, an alternative method is required to accelerate the design. In this study, we applied a quantitative structure-activity relationship (QSAR) to produce a predictive model. The produced model can be used to predict the activity of the compound as an anti-malaria agent. The development of the model was performed by using genetic algorithm (GA) for feature selection and artificial neural network (ANN) for model development. We developed five models by utilizing a different number of the descriptor in each model. The validation process was performed by evaluating several validation parameters, such as accuracy. According to the results, we found that the model 3, which is comprised of seven descriptors, produce a better result with the accuracies of internal and external data set are 0.96 and 0.92, respectively.

We present a simple coarse-grained model with the molecular crowding effect in solvent to investigate the structure and dynamics of protein complexes including association and/or dissociation processes and investigate some physical properties such as the structure and the reaction rate from the viewpoint of the hydrophobic intermolecular interactions of protein complex. In the present coarse-grained model, a function depending upon the density of hydrophobic amino acid residues in a binding area of the complex is introduced, and the function involves the molecular crowding effect for the intermolecular interactions of hydrophobic amino acid residues between proteins. We propose a hydrophobic intermolecular potential energy between proteins by using the density-dependent function. The present coarse-grained model is applied to the complex of cytochrome f and plastocyanin by using the Langevin dynamics simulation to investigate some physical properties such as the complex structure, the electron tra...

We present a simple coarse-grained model of the effective interaction for charged amino acid residues, such as Glu and Lys, in a water solvent. The free-energy profile as a function of the distance between two charged amino acid side-chain analogues in an explicit water solvent is calculated with all-atom molecular dynamics simulation and thermodynamic integration method. The calculated free-energy profile is applied to the coarse-grained potential of the effective interaction between two amino acid residues. The Langevin dynamics simulations with our coarse-grained potential are performed for association of a small protein complex, GCN4-pLI tetramer. The tetramer conformation reproduced by our coarse-grained model is similar to the X-ray crystallographic structure. We show that the effective interaction between charged amino acid residues stabilises association and orientation of protein complex. We also investigate the association pathways of GCN4-pLI tetramer.

Malaria is a disease that commonly infects humans in many tropical areas. This disease becomes a serious problem because of the high resistance of Plasmodium parasite against the well-established antimalarial agents, such as Artemisinin. Hence, new potent compounds are urgently needed to resolve this resistance problem. In the present study, we investigated cycloguanil analogues as a potent antimalarial agent by utilizing several studies, i.e., comparative of molecular field analysis (CoMFA), molecular docking and molecular dynamics (MD) simulation. A CoMFA model with five partial least square regressions (PLSR) was developed to predict the pIC50 value of the compound by utilizing a data set of 42 cycloguanil analogues. From statistical analysis, we obtained the r2 values of the training and test sets that were 0.85 and 0.70, respectively, while q2 of the leave-one-out cross-validation was 0.77. The contour maps of the CoMFA model were also interpreted to analyze the structural requirement regarding electrostatic and steric factors. The most active compound (c33) and least active compound (c8) were picked for molecular docking and MD analysis. From the docking analysis, we found that the attached substituent on the backbone structure of cycloguanil gives a significant contribution to antimalarial activity. The results of the MD simulation confirm the stability of the binding pose obtained from the docking simulations.

Phycocyanin (PC) is the main pigment found in Spirulina platensis and has the potential effect to treat effectively type-2 diabetes mellitus by inhibiting α-amylase and α-glucosidase. However, studies on molecular interactions between PC with α-amylase and α-glucosidase enzymes are still rare. In this study, an in-silico study was carried out to predict the molecular interactions between PC with α-amylase and α-glucosidase enzymes. Molecular docking simulations indicated that PC inhibits the enzymes by binding to the active site and causing a disruption on substrate-enzyme binding. In both enzymes, PC seem to play a crucial role in establishing the interaction within the cavity of active sites. This result suggested PC as a potential candidate for antidiabetic natural therapeutic agents. An in-vitro inhibition activity test showed that PC inhibits human salivary amylase at average of 51.13 %. A storage stability tests showed that keeping PC in solid-state, absence of lights and low temperature can preserve the bioactivity when used as functional compounds. Taken together, this current result would be useful in elucidating the molecular mechanisms of the interaction between PC and carbohydrate-metabolisms enzymes and contribute to making full use of PC as antidiabetic drug or therapeutic agent. Further confirm on diabetic subjects is indispensable to provide the potential therapeutic of PC as an effective anti-diabetic with less frequent of side effect.