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Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a lethal disease caused by two vector-borne parasites: Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense . The limited number of antitrypanosomal therapies for treating these deadly parasites suffer from toxicity, poor efficacy, and unspecified targets; thus, more and better medicines are needed. We used in silico methods to predict features of the bioactive compound AZ960 that make it an ortholog-specific inhibitor for the extracellular-signal regulated kinase 8 of T. brucei (TbERK8). Our homology models showed that the TbERK8 ATP binding pocket was smaller and more hydrophobic than that of human ERK8 (HsERK8). Molecular docking studies predicted six FDA-approved compounds that would be orthologue-specific inhibitors of HsERK8 or TbERK8. Experimental testing of these compounds identified prednisolone as an HsERK8-specific inhibitor. Sildenafil inhibited TbERK8, as predicted by our binding model. Its impact on TbERK8 activity supports our hypothesis that designing compounds that can exploit differences in the orthologs as buildable scaffolds and expand the repertoire of ortholog-specific antitrypanosomal agents.

Synthetic cavitands and protein cavities have been widely studied as models for ligand recognition. Here we investigate the Met102 → His substitution in the artificial L99A cavity in T4 lysozyme as a Kemp eliminase. The resulting enzyme had k cₐₜ/ K M = 0.43 M ⁻¹ s ⁻¹ and a (k cₐₜ/ K M)/ k ᵤₙcₐₜ = 10 ⁷ at pH 5.0. The crystal structure of this enzyme was determined at 1.30 Å, as were the structures of four complexes of substrate and product analogs. The absence of ordered waters or hydrogen bonding interactions, and the presence of a common catalytic base (His102) in an otherwise hydrophobic, buried cavity, facilitated detailed analysis of the reaction mechanism and its optimization. Subsequent substitutions increased eliminase activity by an additional four-fold. As activity-enhancing substitutions were engineered into the cavity, protein stability decreased, consistent with the stability-function trade-off hypothesis. This and related model cavities may provide templates for studying protein design principles in radically simplified environments.

Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed.

Conformational change in protein–ligand complexes is widely modeled, but the protein accommodation expected on binding a congeneric series of ligands has received less attention. Given their use in medicinal chemistry, there are surprisingly few substantial series of congeneric ligand complexes in the Protein Data Bank (PDB). Here we determine the structures of eight alkyl benzenes, in single-methylene increases from benzene ton-hexylbenzene, bound to an enclosed cavity in T4 lysozyme. The volume of the apo cavity suffices to accommodate benzene but, even with toluene, larger cavity conformations become observable in the electron density, and over the series two other major conformations are observed. These involve discrete changes in main-chain conformation, expanding the site; few continuous changes in the site are observed. In most structures, two discrete protein conformations are observed simultaneously, and energetic considerations suggest that these conformations are low in energy relative to the ground state. An analysis of 121 lysozyme cavity structures in the PDB finds that these three conformations dominate the previously determined structures, largely modeled in a single conformation. An investigation of the few congeneric series in the PDB suggests that discrete changes are common adaptations to a series of growing ligands. The discrete, but relatively few, conformational states observed here, and their energetic accessibility, may have implications for anticipating protein conformational change in ligand design.

We present a method to rapidly identify hydrogen-mediated interactions in proteins (e.g., hydrogen bonds, hydrogen bonds, water-mediated hydrogen bonds, salt bridges, and aromatic π-hydrogen interactions) through heavy atom geometry alone, that is, without needing to explicitly determine hydrogen atom positions using either experimental or theoretical methods. By including specific real (or virtual) partner atoms as defined by the atom type of both the donor and acceptor heavy atoms, a set of unique angles can be rapidly calculated. By comparing the distance between the donor and the acceptor and these unique angles to the statistical preferences observed in the Protein Data Bank (PDB), we were able to identify a set of conserved geometries (15 for donor atoms and 7 for acceptor atoms) for hydrogen-mediated interactions in proteins. This set of identified interactions includes every polar atom type present in the Protein Data Bank except OE1 (glutamate/glutamine sidechain) and a clear geometric preference for the methionine sulfur atom (SD) to act as a hydrogen bond acceptor. This method could be readily applied to protein design efforts.

Background Protein repeats can confound sequence analyses because the repetitiveness of their amino acid sequences lead to difficulties in identifying whether similar repeats are due to convergent or divergent evolution. We noted that the patterns derived from traditional “dot plot” protein sequence self-similarity analysis tended to be conserved in sets of related repeat proteins and this conservation could be quantitated using a Jaccard metric. Results Comparison of these dot plots obviated the issues due to sequence similarity for analysis of repeat proteins. A high Jaccard similarity score was suggestive of a conserved relationship between closely related repeat proteins. The dot plot patterns decayed quickly in the absence of selective pressure with an expected loss of 50% of Jaccard similarity due to a loss of 8.2% sequence identity. To perform method testing, we assembled a standard set of 79 repeat proteins representing all the subgroups in RepeatsDB. Comparison of known repeat and non-repeat proteins from the PDB suggested that the information content in dot plots could be used to identify repeat proteins from pure sequence with no requirement for structural information. Analysis of the UniRef90 database suggested that 16.9% of all known proteins could be classified as repeat proteins. These 13.3 million putative repeat protein chains were clustered and a significant amount (82.9%) of clusters containing between 5 and 200 members were of a single functional type. Conclusions Dot plot analysis of repeat proteins attempts to obviate issues that arise due to the sequence degeneracy of repeat proteins. These results show that this kind of analysis can efficiently be applied to analyze repeat proteins on a large scale.

A conserved, 26-residue sequence [AA(X2)[A/G][G/L](X2)GDV[I/L](X2)[V/L]NGE(X1)V(X6)] and corresponding structure repeating module were identified within the HtrA protease family using a non-redundant set (N = 20) of publicly available structures. While the repeats themselves were far from sequence perfect, they had notable conservation to a statistically significant level. Three or more repetitions were identified within each protein despite being statistically expected to randomly occur only once per 1031 residues. This sequence repeat was associated with a six stranded antiparallel β-barrel module, two of which are present in the core of the structures of the PA clan of serine proteases, while a modified version of this module could be identified in the PDZ-like domains. Automated structural alignment methods had difficulties in superimposing these β-barrels, but the use of a target human HtrA2 structure showed that these modules had an average RMSD across the set of structures of less than 2 Å (mean and median). Our findings support Dayhoff’s hypothesis that complex proteins arose through duplication of simpler peptide motifs and domains.

G protein-coupled receptors (GPCRs) constitute the largest and most frequently used family of molecular drug targets. The simplicity of GPCR drug design results from their common seven-transmembrane-helix topology and well-understood signaling pathways. GPCRs are extremely sensitive to slight changes in the chemical structure of compounds, which allows for the reliable design of highly selective and specific drugs. Only recently has the number of GPCR structures, both in their active and inactive conformations, together with their active ligands, become sufficient to comprehensively apply machine learning in decision support systems to predict compound activity in drug design. Here, we describe GPCRVS, an efficient machine learning system for the online assessment of the compound activity against several GPCR targets, including peptide- and protein-binding GPCRs, which are the most difficult for virtual screening tasks. As a decision support system, GPCRVS evaluates compounds in terms of their activity range, the pharmacological effect they exert on the receptor, and the binding mode they could demonstrate for different types and subtypes of GPCRs. GPCRVS allows for the evaluation of compounds ranging from small molecules to short peptides provided in common chemical file formats. The results of the activity class assignment and the binding affinity prediction are provided in comparison with predictions for known active ligands of each included GPCR. Multiclass classification in GPCRVS, handling incomplete and fuzzy biological data, was validated on ChEMBL and Google Patents-retrieved data sets for class B GPCRs and chemokine CC and CXC receptors.

Synthetic cavitands and protein cavities have been widely studied as models for ligand recognition. Here we investigate the Met102 ... His substitution in the artificial L99A cavity in T4 lysozyme as a Kemp eliminase. The resulting enzyme had k.../K... = 0.43 M... s... and a (k.../K...)/k... = 10... at pH 5.0. The crystal structure of this enzyme was determined at 1.30 A, as were the structures of four complexes of substrate and product analogs. The absence of ordered waters or hydrogen bonding interactions, and the presence of a common catalytic base (His102) in an otherwise hydrophobic, buried cavity, facilitated detailed analysis of the reaction mechanism and its optimization. Subsequent substitutions increased eliminase activity by an additional four-fold. As activity-enhancing substitutions were engineered into the cavity, protein stability decreased, consistent with the stability-function trade-off hypothesis. This and related model cavities may provide templates for studying protein design principles in radically simplified environments. (ProQuest: ... denotes formulae/symbols omitted.)

Metformin, an established first-line treatment for patients with type 2 diabetes, has been associated with gastrointestinal (GI) adverse effects that limit its use. Histamine and serotonin have potent effects on the GI tract. The effects of metformin on histamine and serotonin uptake were evaluated in cell lines overexpressing several amine transporters (OCT1, OCT3 and SERT). Metformin inhibited histamine and serotonin uptake by OCT1, OCT3 and SERT in a dose-dependent manner, with OCT1-mediated amine uptake being most potently inhibited (IC 50  = 1.5 mM). A chemoinformatics-based method known as Similarity Ensemble Approach predicted diamine oxidase (DAO) as an additional intestinal target of metformin, with an E-value of 7.4 × 10 −5 . Inhibition of DAO was experimentally validated using a spectrophotometric assay with putrescine as the substrate. The K i of metformin for DAO was measured to be 8.6 ± 3.1 mM. In this study, we found that metformin inhibited intestinal amine transporters and DAO at concentrations that may be achieved in the intestine after therapeutic doses. Further studies are warranted to determine the relevance of these interactions to the adverse effects of metformin on the gastrointestinal tract.