Sequence-based design and construction of synthetic nanobody library.

Nanobody (Nb), the smallest antibody fragments known to bind antigens, is now widely applied to various studies, including protein structure analysis, bioassay, diagnosis, and biomedicine. The traditional approach to generating specific nanobodies involves animal immunization which is time-consuming and expensive. As the understanding of the antibody repertoire accumulation, the synthetic library, which is devoid of animals, has attracted attention widely in recent years. Here, we describe a synthetic phage display library (S-Library), designed based on the systematic analysis of the next-generation sequencing (NGS) of nanobody repertoire. The library consists of a single highly conserved scaffold (IGHV3S65*01-IGHJ4*01) and complementary determining regions of constrained diversity. The S-Library containing 2.19 × 108 independent clones was constructed by the one-step assembly and rapid electro-transformation. The S-Library was screened against various targets (Nb G8, fusion protein of Nb G8 and green fluorescent protein, bovine serum albumin, ovalbumin, and acetylcholinesterase). In comparison, a naïve library (N-Library) from the source of 13 healthy animals was constructed and screened against the same targets as the S-Library. Binders were isolated from both S-Library and N-Library. The dynamic affinity was evaluated by the biolayer interferometry. The data confirms that the feature of the Nb repertoire is conducive to reducing the complexity of library design, thus allowing the S-Library to be built on conventional reagents and primers.

RPfam: A refiner towards curated-like multiple sequence alignments of the Pfam protein families.

High-quality multiple sequence alignments can provide insights into the architecture and function of protein families. The existing MSA tools often generate results inconsistent with biological distribution of conserved regions because of positioning amino acid residues and gaps only by symbols. We propose RPfam, a refiner towards curated-like MSAs for modeling the protein families in the Pfam database. RPfam refines the automatic alignments via scoring alignments based on the PFASUM matrix, restricting realignments within badly aligned blocks, optimizing the block scores by dynamic programming, and running refinements iteratively using the Simulated Annealing algorithm. Experiments show RPfam effectively refined the alignments produced by the MSA tools ClustalO and Muscle with reference to the curated seed alignments of the Pfam protein families. Especially RPfam improved the quality of the ClustalO alignments by 4.4% and the Muscle alignments by 2.8% on the gp32 DNA binding protein-like family. Supplementary Table is available at http://www.worldscinet.com/jbcb/.

Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy.

A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.