Hincer, A. | Search bioRxiv

bioRxiv2023

Synergistic Screening of Peptide-Based Biotechnological Drug Candidates for Neurodegenerative Diseases using Yeast Display and Phage Display

Peptide therapeutics are robust and promising molecules for treating diverse disease conditions. These molecules can be developed from naturally occurring or mimicking native peptides, through rational design and peptide libraries. We developed a new platform for the rapid screening of the peptide therapeutics for disease targets. In the course of the study, we aimed to employ our platform to screen a new generation of peptide therapeutics candidates against aggregation prone protein targets. Two peptide drug candidates for the protein aggregation prone diseases namely Parkinsons and Alzheimers diseases were screened. Currently, there are several therapeutic applications that are only effective in masking or slowing down symptom development. Nonetheless, different approaches are developed for inhibiting amyloid aggregation in the secondary nucleation phase, which is critical for amyloid fibril formation. Instead of targeting secondary nucleated protein structures, we tried to inhibit monomeric amyloid units as a novel approach for halting disease-condition. To achieve this, we combined yeast surface display and phage display library platforms. We expressed -synuclein, amyloid {beta}40, and amyloid {beta}42 on yeast surface, and we selected peptides by using phage display library. After iterative biopanning cycles optimized for yeast cells, several peptides were selected for interaction studies. All of the peptides have been used in vitro characterization methods which are QCM-D measurement, AFM imaging, and ThT assay, and they have yielded promising results in order to block fibrillization or interact with amyloid units as a sensor molecule candidate. Therefore, peptides are good choice for diverse disease-prone molecule inhibition particularly those inhibiting fibrillization. Additionally, these selected peptides can be used as drugs and sensors to detect disease quickly and halt disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/536742v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@50c81dorg.highwire.dtl.DTLVardef@181fea5org.highwire.dtl.DTLVardef@17539c7org.highwire.dtl.DTLVardef@1244a00_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Learning to Generate 5' UTR Sequences for Optimized Ribosome Load and Gene Expression

The 5 untranslated region (5 UTR) of mRNA is crucial for the molecules translatability and stability, making it essential for designing synthetic biological circuits for high and stable protein expression. Several UTR sequences are patented and widely used in laboratories. This paper presents UTRGAN, a Generative Adversarial Network (GAN)-based model for generating 5 UTR sequences, coupled with an optimization procedure to ensure high expression for target gene sequences or high ribosome load and translation efficiency. The model generates sequences mimicking various properties of natural UTR sequences and optimizes them to achieve (i) up to 5-fold higher average expression on target genes, (ii) up to 2-fold higher mean ribosome load, and (iii) a 34-fold higher average translation efficiency compared to initial UTR sequences. UTRGAN-generated sequences also exhibit higher similarity to known regulatory motifs in regions such as internal ribosome entry sites, upstream open reading frames, G-quadruplexes, and Kozak and initiation start codon regions. In-vitro experiments show that the UTR sequences designed by UTRGAN result in a higher translation rate for the human TNF- protein compared to the human Beta Globin 5 UTR, a UTR with high production capacity.

bioinformatics↗