In-solution buffer-free digestion for the analysis of SARS-CoV-2 RBD proteins allows a full sequence coverage and detection of post-translational modifications in a single ESI-MS spectrum

Subunit vaccines based on the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2, are among the most promising strategies to fight the COVID-19 pandemic. The detailed characterization of the protein primary structure by mass spectrometry (MS) is mandatory, as described in ICHQ6B guidelines. In this work, several recombinant RBD proteins produced in five expression systems were characterized using a non-conventional protocol known as in-solution buffer-free digestion (BFD). In a single ESI-MS spectrum, BFD allowed very high sequence coverage (≥ 99 %) and the detection of highly hydrophilic regions, including very short and hydrophilic peptides (2-8 amino acids), the His6-tagged C-terminal peptide carrying several post-translational modifications at Cys538 such as cysteinylation, glutathionylation, cyanilation, among others. The analysis using the conventional digestion protocol allowed lower sequence coverage (80-90 %) and did not detect peptides carrying some of the above-mentioned post-translational modifications. The two C-terminal peptides of a dimer [RBD(319-541)-(His)6]2 linked by an intermolecular disulfide bond (Cys538-Cys538) with twelve histidine residues were only detected by BFD. This protocol allows the detection of the four disulfide bonds present in the native RBD and the low-abundance scrambling variants, free cysteine residues, O-glycoforms and incomplete processing of the N-terminal end, if present. Artifacts that might be generated by the in-solution BFD protocol were also characterized. BFD can be easily implemented and we foresee that it can be also helpful to the characterization of mutated RBD.

Post-Translational Modifications That Drive Prostate Cancer Progression

While a protein primary structure is determined by genetic code, its specific functional form is mostly achieved in a dynamic interplay that includes actions of many enzymes involved in post-translational modifications. This versatile repertoire is widely used by cells to direct their response to external stimuli, regulate transcription and protein localization and to keep proteostasis. Herein, post-translational modifications with evident potency to drive prostate cancer are explored. A comprehensive list of proteome-wide and single protein post-translational modifications and their involvement in phenotypic outcomes is presented. Specifically, the data on phosphorylation, glycosylation, ubiquitination, SUMOylation, acetylation, and lipidation in prostate cancer and the enzymes involved are collected. This type of knowledge is especially valuable in cases when cancer cells do not differ in the expression or mutational status of a protein, but its differential activity is regulated on the level of post-translational modifications. Since their driving roles in prostate cancer, post-translational modifications are widely studied in attempts to advance prostate cancer treatment. Current strategies that exploit the potential of post-translational modifications in prostate cancer therapy are presented.

Function Prediction from Protein Primary Structure using Deep Learning LSTM Algorithm

Biological information of protein primary structure is responsible for finding the protein function, extracting features and function of a protein in the biology lab is challenging and time-consuming. Identification of protein function provides essential information for the treatment of various diseases and drug design. Therefore, extracting the protein knowledge from primary structure alone has been a diverse field in the study of bioinformatics data mining and computational biology. This study aimed to function prediction of protein primary structure using the LSTM methods. PRNP(prion protein )most of the nervous system tissues express by prion protein, this is generally to protease-resistant from disease, due to this reasons, the human codon PRNP is most closely associated with Alzheimer disease. The PRNP protein data trained with Hemo sapiens PRNP selection, classification was implemented with network layer perceptron. The learning algorithms are frame by the nervous system. The training results observation indicate that the learning success of prion protein classification leads positively.

Gaussian-distributed codon frequencies of genomes

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. Frequencies of codon occurrence when ordered in descending sequence provide a global characterization of a genome’s preference (bias) for using the different codons of the redundant genetic code. Whereas frequency/rank relations have been described by empirical relations, here we propose a statistical model in which two different forms of codon usage co-exist in a genome. We investigate whether such a model can account for the range of codon usages observed in a large set of genomes from different taxa. The differences in frequency/rank relations across these genomes can be expressed in a single parameter, the proportion of the two codon compartments. One compartment uses different codons with weak bias according to a Gaussian distribution of frequency, the other uses different codons with strong bias. In prokaryotic genomes both compartments appear to be present in a wide range of proportions, whereas in eukaryotic genomes the compartment with Gaussian distribution tends to dominate. Codon frequencies that are Gaussian-distributed suggest that many evolutionary conditions are involved in shaping weakly-biased codon usage, whereas strong bias in codon usage suggests dominance of few evolutionary conditions.

Codon usage is a stochastic process across genetic codes of the kingdoms of life

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. To uncover rules of codon use, ranked codon frequencies have previously been analyzed in terms of empirical or statistical relations for a small number of genomes. These descriptions fail on most genomes reported in the Codon Usage Tabulated from GenBank (CUTG) database. Here we model codon usage as a random variable. This stochastic model provides accurate, one-parameter characterizations of 2210 nuclear and mitochondrial genomes represented with > 104 codons/genome in CUTG. We show that ranked codon frequencies are well characterized by a truncated normal (Gaussian) distribution. Most genomes use codons in a nearuniform manner. Lopsided usages are also widely distributed across genomes but less frequent. Our model provides a universal framework for investigating determinants of codon use.

Regulatory Activities of Four ArsR Proteins in Agrobacterium tumefaciens 5A

ABSTRACTArsR is a well-studied transcriptional repressor that regulates microbe-arsenic interactions. Most microorganisms have anarsRgene, but in cases where multiple copies exist, the respective roles or potential functional overlap have not been explored. We examined the repressors encoded byarsR1andarsR2(ars1operon) and byarsR3andarsR4(ars2operon) inAgrobacterium tumefaciens5A. ArsR1 and ArsR4 are very similar in their primary sequences and diverge phylogenetically from ArsR2 and ArsR3, which are also quite similar to one another. Reporter constructs (lacZ) forarsR1,arsR2, andarsR4were all inducible by As(III), but expression ofarsR3(monitored by reverse transcriptase PCR) was not influenced by As(III) and appeared to be linked transcriptionally to an upstreamlysR-type gene. Experiments using a combination of deletion mutations and additional reporter assays illustrated that the encoded repressors (i) are not all autoregulatory as is typically known for ArsR proteins, (ii) exhibit variable control of each other's encoding genes, and (iii) exert variable control of other genes previously shown to be under the control of ArsR1. Furthermore, ArsR2, ArsR3, and ArsR4 appear to have an activator-like function for some genes otherwise repressed by ArsR1, which deviates from the well-studied repressor role of ArsR proteins. The differential regulatory activities suggest a complex regulatory network not previously observed in ArsR studies. The results indicate that fine-scale ArsR sequence deviations of the reiterated regulatory proteins apparently translate to different regulatory roles.IMPORTANCEGiven the significance of the ArsR repressor in regulating various aspects of microbe-arsenic interactions, it is important to assess potential regulatory overlap and/or interference when a microorganism carries multiple copies ofarsR. This study explores this issue and shows that the fourarsRgenes inA. tumefaciens5A, associated with two separatearsoperons, encode proteins exhibiting various degrees of functional overlap with respect to autoregulation and cross-regulation, as well as control of other functional genes. In some cases, differences in regulatory activity are associated with only limited differences in protein primary structure. The experiments summarized herein also present evidence that ArsR proteins appear to have activator functions, representing novel regulatory activities for ArsR, previously known only to be a repressor.