Analysis of B-cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Analyzing B-cell receptor (BCR) repertoires is immensely useful in evaluating one's immunological status. Conventionally,repertoire analysis methods have focused on comprehensive assessment of clonal compositions, including V(D)J segment usage, nucleotide insertion/deletion, and amino acid distribution. Here, we introduce a novel computational approach that applies deep-learning based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that our new approach enables us to not only accurately cluster repertoires of COVID-19 patients and healthy subjects, but also efficiently track minute changes in immunity conditions as patients undergo a course of treatment over time. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved over 87% mean accuracy rate given a repertoire of CDR3 sequences.

Correlation between equi-partition of aminoacyl-tRNA synthetases and amino-acid biosynthesis pathways

The partition of aminoacyl-tRNA synthetases (aaRSs) into two classes of equal size and the correlated amino acid distribution is a puzzling still unexplained observation. We propose that the time scale of the amino-acid synthesis, assumed to be proportional to the number of reaction steps (NE) involved in the biosynthesis pathway, is one of the parameters that controlled the timescale of aaRSs appearance. Because all pathways are branched at fructose-6-phosphate on the metabolic pathway, this product is defined as the common origin for the NE comparison. For each amino-acid, the NE value, counted from the origin to the final product, provides a timescale for the pathways to be established. An archeological approach based on NE reveals that aaRSs of the two classes are generated in pair along this timescale. The results support the coevolution theory for the origin of the genetic code with an earlier appearance of class II aaRSs.

Protein structure prediction and design in a biologically-realistic implicit membrane

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

Phytochemical Constituents of Some Vegetables

Alkaloids, terpenoids, tannins, saponins, phlobatannins, flavonoids, steroids, cardiac glycosides, sugar molecules, and amino acid distribution in four vegetables of Bangladesh origin belonging to the family of Cucurbitaceae were assessed and compared. The vegetables investigated were Luffa acutangula (Jhingga), Luffa cylindrica (Dhundul), Trichosanthes anguina (Chichingga) and Tricosanthes dioica (Potol). DOI: http://dx.doi.org/10.3329/dujs.v61i2.17060 Dhaka Univ. J. Sci. 61(2): 147-151, 2013 (July)

Amino acid distribution rules predict protein fold

In the present article, we provide a brief overview of the main approaches to analysing the sequence–structure relationship of proteins and outline a novel method of structure prediction. The proposed method involves finding a set of rules that describes a correlation between the distribution of residues in a sequence and the essential structural characteristics of a protein structure. The residue distribution rules specify the ‘favourable’ residues that are required in certain positions of a polypeptide chain in order for it to assume a particular protein fold, and the ‘unfavourable’ residues incompatible with the given fold. Identification of amino acid distribution rules derives from examination of inter-residue contacts. We describe residue distribution rules for a large group of β-sandwich-like proteins characterized by a specific arrangement of strands in their two β-sheets. It was shown that this method has very high accuracy (approximately 85%). The advantage of the residue rule approach is that it makes possible prediction of protein folding even in polypeptide chains that have very low global sequence similarities, as low as 18%. Another potential benefit is that a better understanding of which residues play essential roles in a given protein fold may facilitate rational protein engineering design.