The crystal structure of holo D-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) from the extreme thermophile Thermus aquaticus has been solved at 2.5 Angstroms resolution. To study the determinants of thermostability, we compare our structure to four other GAPDHs. Salt links, hydrogen bonds, buried surface area, packing density, surface to volume ratio, and stabilization of alpha-helices and beta-turns are analyzed. We find a strong correlation between thermostability and the number of hydrogen bonds between charged side chains and neutral partners. These charged-neutral hydrogen bonds provide electrostatic stabilization without the heavy desolvation penalty of salt links. The stability of thermophilic GAPDHs is also correlated with the number of intrasubunit salt links and total hydrogen bonds. Charged residues, therefore, play a dual role in stabilization by participating not only in salt links but also in hydrogen bonds with a neutral partner. Hydrophobic effects allow for discrimination between thermophiles and psychrophiles, but not within the GAPDH thermophiles. There is, however, an association between thermostability and decreasing enzyme surface to volume ratio. Finally, we describe several interactions present in both our GAPDH and a hyperthermophilic GAPDH that are absent in the less thermostable GAPDHs. These include a four-residue salt link network, a hydrogen bond near the active site, an intersubunit salt link, and several buried Ile residues.

The crystal structure of Serratia endonuclease has been solved to 2.1 A by multiple isomorphous replacement. This magnesium-dependent enzyme is equally active against single- and double-stranded DNA, as well as RNA, without any apparent base preference. The Serratia endonuclease fold is distinct from that of other nucleases that have been solved by X-ray diffraction. The refined structure consists of a central layer containing six antiparallel beta-strands which is flanked on one side by a helical domain and on the opposite side by one dominant helix and a very long coiled loop. Electrostatic calculations reveal a strongly polarized molecular surface and suggest that a cleft between this long helix and loop, near His 89, may contain the active site of the enzyme.

1 Background 2 An important task of macromolecular structure determination by cryo-electron 3 microscopy (cryo-EM) is the identification of single particles in micrographs (particle 4 picking). Currently, particle picking is laborious, time consuming, and potentially biased 5 due to the need of human intervention to initialize the particle picking. The results 6 typically include many false positives and negatives. Adjusting the parameters to 7 eliminate false positives often excludes true particles in certain orientations. The 8 supervised machine learning (e.g. deep learning) methods for particle picking often 9 need a large training dataset, which requires extensive manual annotation. Other 10 reference-dependent methods rely on low-resolution templates for particle detection, 11 matching and picking, and therefore, are not fully automated. These issues motivate 12 us to develop a fully automated, unbiased framework for particle picking. 13Results 14 We design a fully automated, unsupervised approach for single particle picking in cryo-15 EM micrographs. Our approach consists of three stages: image preprocessing, particle 16 clustering, and particle picking. The image preprocessing is based on image 17 averaging, normalization, cryo-EM image contrast enhancement correction (CEC), 18 histogram equalization, restoration, adaptive histogram equalization, guided image 19 filtering, and morphological operations significantly improves the quality of original 20 cryo-EM images. Our particle clustering method is based on an intensity distribution 21 model which is much faster and more accurate than traditional K-means and Fuzzy C-22 Means (FCM) algorithms for single particle clustering. Our particle picking method, 23 based on image cleaning and shape detection with a modified Circular Hough 24 Transform algorithm, effectively detects the shape and the center of each particle and 25 creates a bounding box encapsulating the particles. 26 Conclusions 27AutoCryoPicker can automatically and effectively recognizes particle-like objects from 28 in noisy cryo-EM micrographs without the need of labeled training data and human 29 intervention and therefore is a useful tool for cryo-EM protein structure determination. 30 Keywords 31Clustering, Intensity Based Clustering (IBC), micrograph, Cryo-EM, singe particle 32 pickling, protein structure determination. -3 - Background 34For decades, X-ray crystallography has been the dominant technique for obtaining 35 high-resolution structures of macromolecules. Single-particle cryo-electron 36 microscopy (cryo-EM) was traditionally used to provide low resolution structural 37 information on large protein complexes that resisted crystallization (e.g., highly 38 symmetric particles of viruses). Though the basic workflow of cryo-EM has not 39 changed considerably over the years, recent technological advances in sample 40 preparation, computation and especially instrumentation have revolutionized the field 41 of structural biology [1] [2] [3], allowing it to solve large protein struct...

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions.

Context. The stellar abundance ratio of Mg/Fe is an important tool in diagnostics of galaxy evolution. In order to make reliable measurements of the Mg abundance of stars, it is necessary to have accurate values for the oscillator strength ( f -value) of each of the observable transitions. In metal-poor stars the Mg i 3p-4s triplet around 5175 Å (Fraunhofer's so-called b lines) are the most prominent magnesium lines. The lines also appear as strong features in the solar spectrum.Aims. We present new and improved experimental oscillator strengths for the optical Mg i 3p-4s triplet, along with experimental radiative lifetimes for six terms in Mg i. With these data we discuss the implications on previous and future abundance analyses of metal-poor stars. Methods. The oscillator strengths have been determined by combining radiative lifetimes with branching fractions, where the radiative lifetimes are measured using the laser induced fluorescence technique and the branching fractions are determined using intensity calibrated Fourier Transform (FT) spectra. The FT spectra are also used for determining new accurate laboratory wavelengths for the 3p-4s transitions.Results. The f -values of the Mg i 3p-4s lines have been determined with an absolute uncertainty of 9%, giving an uncertainty of ±0.04 dex in the log g f values. Compared to values previously used in abundance analyses of metal-poor stars, rescaling to the new values implies an increase of typically 0.04 dex in the magnesium abundance.