AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to accurately prepare the input structure files and to optimally define the search space, offering several alternatives and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it automatically with several predefined visualization schemes to aid in setting up the box defining the search space and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock’s functional versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS. Reviewers: This article was reviewed by Alexander Krah and Thomas Gaillard.

Single domain antibodies from camelids, or nanobodies, are a unique class of antibody fragments with several advantageous characteristics: small monomeric size, high stability and solubility and easy tailoring for multiple applications. Nanobodies are gaining increasing acceptance as diagnostic tools and promising therapeutic agents in cancer and other diseases. While most nanobodies are obtained from immunized animals of the camelid family, a few synthetic nanobody libraries constructed in recent years have shown the capability of generating high quality nanobodies in terms of affinity and stability. Since this synthetic approach has important advantages over the use of animals, the recent advances are indeed encouraging. Here we review over a dozen synthetic nanobody libraries reported so far and discuss the different approaches followed in their construction and validation, with an emphasis on framework and hypervariable loop design as critical issues defining their potential as high-class nanobody sources.

We present NbThermo—a first-in-class database that collects melting temperatures (T_m), amino acid sequences and several other categories of useful data for hundreds of nanobodies (Nbs), compiled from an extensive literature search. This so-far unique database currently contains up-to-date, manually curated data for 564 Nbs. It represents a contribution to efforts aimed at developing new algorithms for reliable T_m prediction to assist Nb engineering for a wide range of applications of these unique biomolecules. Nbs from the two most common source organisms—llama and camel—show similar distributions of melting temperatures. A first exploratory research that takes advantage of this large data collection evidences that understanding the structural bases of Nb thermostability is a complex task, since there are no apparent differences in sequence patterns between the frameworks of Nbs with lower and higher melting temperatures, indicating that the highly variable loops play a relevant role in defining Nb thermostability. Database URLhttps://valdes-tresanco-ms.github.io/NbThermo

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Objective: To design and construct a new synthetic nanobody library using a structure-based approach that seeks to maintain high protein stability and increase the number of functional variants within the combinatorial space of mutations.Results: Synthetic nanobody (Nb) libraries are emerging as an attractive alternative to animal immunization for the selection of stable, high affinity Nbs. Two key features define a synthetic Nb library: framework selection and CDR design. We selected the universal VHH framework from the cAbBCII10 Nb. CDR1 and CDR2 were designed with the same fixed length as in cAbBCII10, while for CDR3 we chose a 14-long loop, which creates a convex binding site topology. Based on the analysis of the cAbBCII10 crystal structure, we carefully selected the positions to be randomized and tailored the codon usage at each position, keeping at particular places amino acids that guarantee stability, favoring properties like polarity at solvent-exposed positions and avoiding destabilizing amino acids. Gene synthesis and library construction were carried out by GenScript, using our own phagemid vector. The constructed library has an estimated size of 1.75 × 10⁸. NGS showed that the amino acid diversity and frequency at each randomized position are the expected from the codon usage.