Searching similar local 3D micro-environments in protein structure databases with MicroMiner.

The available protein structure data are rapidly increasing. Within these structures, numerous local structural sites depict the details characterizing structure and function. However, searching and analyzing these sites extensively and at scale poses a challenge. We present a new method to search local sites in protein structure databases using residue-defined local 3D micro-environments. We implemented the method in a new tool called MicroMiner and demonstrate the capabilities of residue micro-environment search on the example of structural mutation analysis. Usually, experimental structures for both the wild-type and the mutant are unavailable for comparison. With MicroMiner, we extracted $>255 \times 10^{6}$ amino acid pairs in protein structures from the PDB, exemplifying single mutations' local structural changes for single chains and $>45 \times 10^{6}$ pairs for protein-protein interfaces. We further annotate existing data sets of experimentally measured mutation effects, like $\Delta \Delta G$ measurements, with the extracted structure pairs to combine the mutation effect measurement with the structural change upon mutation. In addition, we show how MicroMiner can bridge the gap between mutation analysis and structure-based drug design tools. MicroMiner is available as a command line tool and interactively on the https://proteins.plus/ webserver.

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies.

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on the dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. Our derived Fi-score can be used to classify protein regions or individual structural subsets of interest and the described scoring system could be integrated into other discovery pipelines, such as protein classification databases, or applied to investigate new targets. We further demonstrated how our method can be integrated into machine learning Gaussian mixture models to predict different structural elements. Fi-score, in combination with other biophysical analytical methods depending on the research goals, could help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites. The described methodology could greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets. HIGHLIGHTSDescription and derivation of a first-of-its-kind in silico protein fingerprinting method using B-factors and dihedral angles.Derived Fi-score allows to characterise the whole protein or selected regions of interest.Demonstration how machine learning using Gaussian mixture models on Fi-scores captures and allows to predict functional protein topology elements.Fi-score is a novel method to help evaluate therapeutic targets and engineer effective biologics.Communicated by Ramaswamy H. Sarma.

Variable posttranslational modifications of severe acute respiratory syndrome coronavirus 2 nucleocapsid protein.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), started in 2019 in China and quickly spread into a global pandemic. Nucleocapsid protein (N protein) is highly conserved and is the most abundant protein in coronaviruses and is thus a potential target for both vaccine and point-of-care diagnostics. N Protein has been suggested in the literature as having posttranslational modifications (PTMs), and accurately defining these PTMs is critical for its potential use in medicine. Reports of phosphorylation of N protein have failed to provide detailed site-specific information. We have performed comprehensive glycomics, glycoproteomics and proteomics experiments on two different N protein preparations. Both were expressed in HEK293 cells; one was in-house expressed and purified without a signal peptide (SP) sequence, and the other was commercially produced with a SP channeling it through the secretory pathway. Our results show completely different PTMs on the two N protein preparations. The commercial product contained extensive N- and O-linked glycosylation as well as O-phosphorylation on site Thr393. Conversely, the native N Protein model had O-phosphorylation at Ser176 and no glycosylation, highlighting the importance of knowing the provenance of any commercial protein to be used for scientific or clinical studies. Recent studies have indicated that N protein can serve as an important diagnostic marker for COVID-19 and as a major immunogen by priming protective immune responses. Thus, detailed structural characterization of N protein may provide useful insights for understanding the roles of PTMs on viral pathogenesis, vaccine design and development of point-of-care diagnostics.

Site-Specific Amino Acid Distributions Follow a Universal Shape.

In many applications of evolutionary inference, a model of protein evolution needs to be fitted to the amino acid variation at individual sites in a multiple sequence alignment. Most existing models fall into one of two extremes: Either they provide a coarse-grained description that lacks biophysical realism (e.g., dN/dS models), or they require a large number of parameters to be fitted (e.g., mutation-selection models). Here, we ask whether a middle ground is possible: Can we obtain a realistic description of site-specific amino acid frequencies while severely restricting the number of free parameters in the model? We show that a distribution with a single free parameter can accurately capture the variation in amino acid frequency at most sites in an alignment, as long as we are willing to restrict our analysis to predicting amino acid frequencies by rank rather than by amino acid identity. This result holds equally well both in alignments of empirical protein sequences and of sequences evolved under a biophysically realistic all-atom force field. Our analysis reveals a near universal shape of the frequency distributions of amino acids. This insight has the potential to lead to new models of evolution that have both increased realism and a limited number of free parameters.

Expression,Purification and Antibody Preparation of Recombinant C8orf32 Protein / 中国生物工程杂志

C8orf32 is a gene which has not been functionally characterized,the mRNA level of this gene is significantly higher in breast cancer tissues than that in normal breast tissues.The amplified cDNA fragment was inserted into the pGEX-6P1 vector fused with the upstream GST gene.The expression vector was transformed into the E.coli BL21(DE3) strain and expression of GST-C8orf32 fusion protein was induced by IPTG..After removal of GST tag by site-specific protease,the C8orf32 protein fused with an eight amino acid peptide tag was obtained.The purity of recombinant C8orf32 protein was about 95%.The identity of the purified protein was confirmed by N-terminal sequencing and tandem mass spectrometry.The polyclonal antibody was prepared by immunizing the New Zealand white rabbits with C8orf32 protein.The polyclonal antibody was proved to recognize the C8orf32 protein correctly.The purified C8orf32 protein can be used for structural and functional studies and the polyclonal antibody can be used for tissue specific protein expression profiling.