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The elucidation of the three dimensional structure of biological macromolecules has provided an important contribution to our current understanding of many basic mechanisms involved in life processes. This enormous impact largely results from the ability of X-ray crystallography to provide accurate structural details at atomic resolution that are a prerequisite for a deeper insight on the way in which bio-macromolecules interact with each other to build up supramolecular nano-machines capable of performing specialized biological functions. With the advent of high-energy synchrotron sources and the development of sophisticated software to solve X-ray and neutron crystal structures of large molecules, the crystallization step has become even more the bottleneck of a successful structure determination. This review introduces the general aspects of protein crystallization, summarizes conventional and innovative crystallization methods and focuses on the new strategies utilized to improve the success rate of experiments and increase crystal diffraction quality. [ABSTRACT FROM AUTHOR]

Serum albumin is one of the most widely studied proteins. It is the most abundant protein in plasma with a typical concentration of 5 g/100 mL and the principal transporter of fatty acids in plasma. While the crystal structures of human serum albumin (HSA) free and in complex with fatty acids, hemin, and local anesthetics have been characterized, no crystallographic models are available on bovine serum albumin (BSA), presumably because of the poor diffraction power of existing hexagonal BSA crystals. Here, the crystallization and diffraction data of a new BSA crystal form, obtained by the hanging drop method using MPEG 5K as precipitating agent, are presented. The crystals belong to space group C2, with unit-cell parameters a = 216.45 Å, b = 44.72 Å, c = 140.18 Å, β = 114.5°. Dehydration was found to increase the diffraction limit of BSA crystals from ~8 Å to 3.2 Å, probably by improving the packing of protein molecules in the crystal lattice. These results, together with a survey of more than 60 successful cases of protein crystal dehydration, confirm that it can be a useful procedure to be used in initial screening as a method of improving the diffraction limits of existing crystals. [ABSTRACT FROM AUTHOR]

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization. [ABSTRACT FROM AUTHOR]