Studying the characteristics of nanobody CDR regions based on sequence analysis in combination with 3D structures.

BACKGROUND: Single-domain antibodies or nanobodies have recently attracted much attention in research and applications because of their great potential and advantage over conventional antibodies. However, isolation of candidate nanobodies in the lab has been costly and time-consuming. Screening of leading nanobody candidates through synthetic libraries is a promising alternative, but it requires prior knowledge to control the diversity of the complementarity-determining regions (CDRs) while still maintaining functionality. In this work, we identified sequence characteristics that could contribute to nanobody functionality by analyzing three datasets, CDR1, CDR2, and CDR3. RESULTS: By classification of amino acids based on physicochemical properties, we found that two different amino acid groups were sufficient for CDRs. The nonpolar group accounted for half of the total amino acid composition in these sequences. Observation of the highest occurrence of each amino acid revealed that the usage of some important amino acids such as tyrosine and serine was highly correlated with the length of the CDR3. Amino acid repeat motifs were also under-represented and highly restricted as 3-mers. Inspecting the crystallographic data also demonstrated conservation in structural coordinates of dominant amino acids such as methionine, isoleucine, valine, threonine, and tyrosine and certain positions in the CDR1, CDR2, and CDR3 sequences. CONCLUSIONS: We identified sequence characteristics that contributed to functional nanobodies including amino acid groups, the occurrence of each kind of amino acids, and repeat patterns. These results provide a simple set of rules to make it easier to generate desired candidates by computational means; also, they can be used as a reference to evaluate synthetic nanobodies.

A New Pathway for the Preparation of Pyrano[2,3-c]pyrazoles and molecular Docking as Inhibitors of p38 MAP Kinase.

We report a new pathway to synthesize pyrano[2,3-c]pyrazoles and their binding mode to p38 MAP kinase. Pyrano[2,3-c]pyrazole derivatives have been prepared through a four-component reaction of benzyl alcohols, ethyl acetoacetate, phenylhydrazine, and malononitrile in the presence of sulfonated amorphous carbon and eosin Y as catalysts. All products were characterized by melting point, 1H and 13C NMR, and HRMS (ESI). The products were screened in silico for their binding activities to both the ATP-binding pocket and the lipid-binding pocket of p38 MAP kinase, using a structure-based flexible docking provided by the engine ADFR. The results showed that eight synthesized compounds had a higher affinity to the lipid pocket than to the other target site, which implied potential applications as allosteric inhibitors. Finally, the most biologically active compound, 5, had a binding affinity comparable to those of other proven lipid pocket inhibitors, with affinity to the target pocket reaching -10.9932 kcal/mol, and also had the best binding affinity to the ATP-binding pockets in all of our products. Thus, our research provides a novel pathway for synthesizing pyrano[2,3-c]pyrazoles and bioinformatic evidence for their biological capability to block p38 MAP kinase pockets, which could be useful for developing cancer or immune drugs.

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.