PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/photomod

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation

Computational Design of Hypothetical New Peptides Based on a Cyclotide Scaffold as HIV gp120 Inhibitor.

Cyclotides are a family of triple disulfide cyclic peptides with exceptional resistance to thermal/chemical denaturation and enzymatic degradation. Several cyclotides have been shown to possess anti-HIV activity, including kalata B1 (KB1). However, the use of cyclotides as anti-HIV therapies remains limited due to the high toxicity in normal cells. Therefore, grafting anti-HIV epitopes onto a cyclotide might be a promising approach for reducing toxicity and simultaneously improving anti-HIV activity. Viral envelope glycoprotein gp120 is required for entry of HIV into CD4+ T cells. However, due to a high degree of variability and physical shielding, the design of drugs targeting gp120 remains challenging. We created a computational protocol in which molecular modeling techniques were combined with a genetic algorithm (GA) to automate the design of new cyclotides with improved binding to HIV gp120. We found that the group of modified cyclotides has better binding scores (23.1%) compared to the KB1. By using molecular dynamic (MD) simulation as a post filter for the final candidates, we identified two novel cyclotides, GA763 and GA190, which exhibited better interaction energies (36.6% and 22.8%, respectively) when binding to gp120 compared to KB1. This computational design represents an alternative tool for modifying peptides, including cyclotides and other stable peptides, as therapeutic agents before the synthesis process

A model of infection in honeybee colonies with social immunity.

Honeybees (Apis mellifera) play a significant role in the pollination of various food crops and plants. In the past decades, honeybee management has been challenged with increased pathogen and environmental pressure associating with increased beekeeping costs, having a marked economic impact on the beekeeping industry. Pathogens have been identified as a contributing cause of colony losses. Evidence suggested a possible route of pathogen transmission among bees via oral-oral contacts through trophallaxis. Here we propose a model that describes the transmission of an infection within a colony when bee members engage in the trophallactic activity to distribute nectar. In addition, we examine two important features of social immunity, defined as collective disease defenses organized by honeybee society. First, our model considers the social segregation of worker bees. The segregation limits foragers, which are highly exposed to pathogens during foraging outside the nest, from interacting with bees residing in the inner parts of the nest. Second, our model includes a hygienic response, by which healthy nurse bees exterminate infected bees to mitigate horizontal transmission of the infection to other bee members. We propose that the social segregation forms the first line of defense in reducing the uptake of pathogens into the colony. If the first line of defense fails, the hygienic behavior provides a second mechanism in preventing disease spread. Our study identifies the rate of egg-laying as a critical factor in maintaining the colony's health against an infection. We propose that winter conditions which cease or reduce the egg-laying activity combined with an infection in early spring can compromise the social immunity defenses and potentially cause colony losses

Clustering analysis of true-negative antagonistic drug pairs based on the protein synergy scores of the genetic algorithm-selected proteins.

The positive scores (green) indicate increased or decreased protein activities due to the drug combination perturbation compared to the single-drug treatment for tumor-suppressor or oncoproteins, respectively. The negative scores (red) indicate vice versa.</p

Construction of Light-Responsive Gene Regulatory Network for Growth, Development and Secondary Metabolite Production in Cordyceps militaris

Cordyceps militaris is an edible fungus that produces many beneficial compounds, including cordycepin and carotenoid. In many fungi, growth, development and secondary metabolite production are controlled by crosstalk between light-signaling pathways and other regulatory cascades. However, little is known about the gene regulation upon light exposure in C. militaris. This study aims to construct a gene regulatory network (GRN) that responds to light in C. militaris. First, a genome-scale GRN was built based on transcription factor (TF)-target gene interactions predicted from the Regulatory Sequence Analysis Tools (RSAT). Then, a light-responsive GRN was extracted by integrating the transcriptomic data onto the genome-scale GRN. The light-responsive network contains 2689 genes and 6837 interactions. From the network, five TFs, Snf21 (CCM_04586), an AT-hook DNA-binding motif TF (CCM_08536), a homeobox TF (CCM_07504), a forkhead box protein L2 (CCM_02646) and a heat shock factor Hsf1 (CCM_05142), were identified as key regulators that co-regulate a large group of growth and developmental genes. The identified regulatory network and expression profiles from our analysis suggested how light may induce the growth and development of C. militaris into a sexual cycle. The light-mediated regulation also couples fungal development with cordycepin and carotenoid production. This study leads to an enhanced understanding of the light-responsive regulation of growth, development and secondary metabolite production in the fungi

Drug target proteins identified from Drugbank and Therapeutic target database.

Drug target proteins identified from Drugbank and Therapeutic target database.</p

Clustering analysis of true-positive synergistic drug pairs based on the protein synergy scores of the genetic algorithm-selected proteins.

The positive scores (green) indicate increased or decreased protein activities due to the drug combination perturbation compared to the single-drug treatment for tumor-suppressor or oncoproteins, respectively. The negative scores (red) indicate vice versa.</p

Scatter plot between actual and predicted HSA synergy scores.

The actual and predicted HSA scores were scaled with the Min-Max normalization method to achieve a score between −1 and +1.</p