Participation of protein sequence termini in crystal contacts

Evolution is not Uniform Along Coding Sequences

Amino acids evolve at different speeds within protein sequences, because their functional and structural roles are different. Notably, amino acids located at the surface of proteins are known to evolve more rapidly than those in the core. In particular, amino acids at the N- and C-termini of protein sequences are likely to be more exposed than those at the core of the folded protein due to their location in the peptidic chain, and they are known to be less structured. Because of these reasons, we would expect that amino acids located at protein termini would evolve faster than residues located inside the chain. Here we test this hypothesis and found that amino acids evolve almost twice as fast at protein termini compared with those in the center, hinting at a strong topological bias along the sequence length. We further show that the distribution of solvent-accessible residues and functional domains in proteins readily explain how structural and functional constraints are weaker at their termini, leading to the observed excess of amino acid substitutions. Finally, we show that the specific evolutionary rates at protein termini may have direct consequences, notably misleading in silico methods used to infer sites under positive selection within genes. These results suggest that accounting for positional information should improve evolutionary models.

Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction

The COVID-19 pandemic has resulted in millions of deaths around the world. Multiple vaccines are in use, but there are many underserved locations that do not have adequate access to them. Variants may emerge that are highly resistant to existing vaccines, and therefore cheap and readily obtainable therapeutics are needed. Phytochemicals, or plant chemicals, can possibly be such therapeutics. Phytochemicals can be used in a polypharmacological approach, where multiple viral proteins are inhibited and escape mutations are made less likely. Finding the right phytochemicals for viral protein inhibition is challenging, but in-silico screening methods can make this a more tractable problem. In this study, we screen a wide range of natural drug products against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow. This workflow consists of a structure-based virtual screening (SBVS), where an initial phytochemical library was docked against all selected protein structures. Subsequently, ligand-based virtual screening (LBVS) was employed, where chemical features of 34 lead compounds obtained from the SBVS were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. A computational docking validation of the 53 predicted leads obtained from LBVS revealed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Collectively, this study demonstrates the advantage of incorporating machine learning elements into a virtual screening workflow.Communicated by Ramaswamy H. Sarma.

Conformational Control of UDP-Galactopyranose Mutase Inhibition

UDP-galactopyranose mutase (Glf or UGM) catalyzes the formation of uridine 5'-diphosphate-α-d-galactofuranose (UDP-Galf) from UDP-galactopyranose (UDP-Galp). The enzyme is required for the production of Galf-containing glycans. UGM is absent in mammals, but members of the Corynebacterineae suborder require UGM for cell envelope biosynthesis. The need for UGM in some pathogens has prompted the search for inhibitors that could serve as antibiotic leads. Optimizing inhibitor potency, however, has been challenging. The UGM from Klebsiella pneumoniae (KpUGM), which is not required for viability, is more effectively impeded by small-molecule inhibitors than are essential UGMs from species such as Mycobacterium tuberculosis or Corynebacterium diphtheriae. Why KpUGM is more susceptible to inhibition than other orthologs is not clear. One potential source of difference is UGM ortholog conformation. We previously determined a structure of CdUGM bound to a triazolothiadiazine inhibitor in the open form, but it was unclear whether the small-molecule inhibitor bound this form or to the closed form. By varying the terminal tag (CdUGM-His₆ and GSG-CdUGM), we crystallized CdUGM to capture the enzyme in different conformations. These structures reveal a pocket in the active site that can be exploited to augment inhibitor affinity. Moreover, they suggest the inhibitor binds the open form of most prokaryotic UGMs but can bind the closed form of KpUGM. This model and the structures suggest strategies for optimizing inhibitor potency by exploiting UGM conformational flexibility.

The "Sticky Patch" Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e., crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure, and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular "sticky patch" model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer "sticky patches" which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the "sticky patch" model. We discuss state-of-the-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43,000 RNA-mediated interactions, and ∼12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.

Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users

It is often necessary to build subsets of the Protein Data Bank to extract structural trends and average values. For this purpose it is mandatory that the subsets are non-redundant and of high quality. The first problem can be solved relatively easily at the sequence level or at the structural level. The second, on the contrary, needs special attention. It is not sufficient, in fact, to consider the crystallographic resolution and other feature must be taken into account: the absence of strings of residues from the electron density maps and from the files deposited in the Protein Data Bank; the B-factor values; the appropriate validation of the structural models; the quality of the electron density maps, which is not uniform; and the temperature of the diffraction experiments. More stringent criteria produce smaller subsets, which can be enlarged with more tolerant selection criteria. The incessant growth of the Protein Data Bank and especially of the number of high-resolution structures is allowing the use of more stringent selection criteria, with a consequent improvement of the quality of the subsets of the Protein Data Bank.

Missing strings of residues in protein crystal structures

A large fraction of the protein crystal structures deposited in the Protein Data Bank are incomplete, since the position of one or more residues is not reported, despite these residues are part of the material that was analyzed. This may bias the use of the protein crystal structures by molecular biologists. Here we observe that in the large majority of the protein crystal structures strings of residues are missing. Polar residues incline to occur in missing strings together with glycine, while apolar and aromatic residues tend to avoid them. Particularly flexible residues, as shown by their extremely high B-factors, by their exposure to the solvent and by their secondary structures, flank the missing strings. These data should be a helpful guideline for crystallographers that encounter regions of flat and uninterpretable electron density as well as end-users of crystal structures.

Frequency of dipeptides and antidipeptides

Although it is reasonable to expect that the frequency of a generic dipeptide XY in proteins is the same of its counterpart YX, on the basis of an accurate statistical analysis of a large number of protein sequences, it appears that some dipeptides XY are considerably more frequent than their mirror images YX, referred to as antidipeptides. Given that it has been verified that this unexpected anisotropic frequency of occurrence is unbiased by the type of protein sequences that are analyzed, it is possible to conclude that this is a genuine phenomenon. Nevertheless, it was impossible to find the mechanism underlying this unexpected phenomenon, which does not seem to be related to diverse conformational propensities, to the different conformational flexibility of the peptide/antidipeptide pair, to dissimilar accessibility to the solvent or to gene random mutations.

DOMMINO: a database of macromolecular interactions

With the growing number of experimentally resolved structures of macromolecular complexes, it becomes clear that the interactions that involve protein structures are mediated not only by the protein domains, but also by various non-structured regions, such as interdomain linkers, or terminal sequences. Here, we present DOMMINO (http://dommino.org), a comprehensive database of macromolecular interactions that includes the interactions between protein domains, interdomain linkers, N- and C-terminal regions and protein peptides. The database complements SCOP domain annotations with domain predictions by SUPERFAMILY and is automatically updated every week. The database interface is designed to provide the user with a three-stage pipeline to study macromolecular interactions: (i) a flexible search that can include a PDB ID, type of interaction, SCOP family of interacting proteins, organism name, interaction keyword and a minimal threshold on the number of contact pairs; (ii) visualization of subunit interaction network, where the user can investigate the types of interactions within a macromolecular assembly; and (iii) visualization of an interface structure between any pair of the interacting subunits, where the user can highlight several different types of residues within the interfaces as well as study the structure of the corresponding binary complex of subunits.