Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2's ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameter's ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins. Significance Statement: Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithm's multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

Nicotinamide riboside activates SIRT5 deacetylation.

Human sirtuins play important roles in various cellular events including DNA repair, gene silencing, mitochondrial biogenesis, insulin secretion and apoptosis. They regulate a wide array of protein and enzyme targets through their NAD+ -dependent deacetylase activities. Sirtuins are also thought to mediate the beneficial effects of low-calorie intake to extend longevity in diverse organisms from yeast to mammals. Small molecules mimicking calorie restriction to stimulate sirtuin activity are attractive therapeutics against age-related disorders such as cardiovascular diseases, diabetes and neurodegeneration. Little is known about one of the mitochondrial sirtuins, SIRT5. SIRT5 has emerged as a critical player in maintaining cardiac health and neuronal viability upon stress and functions as a tumour suppressor in a context-specific manner. Much has been debated about whether SIRT5 has evolved away from being a deacetylase because of its weak catalytic activity, especially in the in vitro testing. We have, for the first time, identified a SIRT5-selective allosteric activator, nicotinamide riboside (NR). It can increase SIRT5 catalytic efficiency with different synthetic peptide substrates. The mechanism of action was further explored using a combination of molecular biology and biochemical strategies. Based on the existing structural biology information, the NR binding site was also mapped out. These activators are powerful chemical probes for the elucidation of cellular regulations and biological functions of SIRT5. The knowledge gained in this study can be used to guide the design and synthesis of more potent, isotype-selective SIRT5 activators and to develop them into therapeutics for metabolic disorders and age-related diseases.

3D interaction homology: The hydrophobic residues alanine, isoleucine, leucine, proline and valine play different structural roles in soluble and membrane proteins.

The aliphatic hydrophobic amino acid residues-alanine, isoleucine, leucine, proline and valine-are among the most common found in proteins. Their structural role in proteins is seemingly obvious: engage in hydrophobic interactions to stabilize secondary, and to a lesser extent, tertiary and quaternary structure. However, favorable hydrophobic interactions involving the sidechains of these residue types are generally less significant than the unfavorable set arising from interactions with polar atoms. Importantly, the constellation of interactions between residue sidechains and their environments can be recorded as three-dimensional maps that, in turn, can be clustered. The clustered average map sets compose a library of interaction profiles encoding interaction strengths, interaction types and the optimal 3D position for the interacting partners. This library is backbone angle-dependent and suggests solvent and lipid accessibility for each unique interaction profile. In this work, in addition to analysis of soluble proteins, a large set of membrane proteins that contained optimized artificial lipids were evaluated by parsing the structures into three distinct components: soluble extramembrane domain, lipid facing transmembrane domain, core transmembrane domain. The aliphatic residues were extracted from each of these sets and passed through our calculation protocol. Notable observations include: the roles of aliphatic residues in soluble proteins and in the membrane protein's soluble domains are nearly identical, although the latter are slightly more solvent accessible; by comparing maps calculated with sidechain-lipid interactions to maps ignoring those interactions, the potential extent of residue-lipid and residue-interactions can be assessed and likely exploited in structure prediction and modeling; amongst these residue types, the levels of lipid engagement show isoleucine as the most engaged, while the other residues are largely interacting with neighboring helical residues.

Novel eIF4A1 inhibitors with anti-tumor activity in lymphoma.

BACKGROUND: Deregulated translation initiation is implicated extensively in cancer initiation and progression. It is actively pursued as a viable target that circumvents the dependency on oncogenic signaling, a significant factor in current strategies. Eukaryotic translation initiation factor (eIF) 4A plays an essential role in translation initiation by unwinding the secondary structure of messenger RNA (mRNA) upstream of the start codon, enabling active ribosomal recruitment on the downstream genes. Several natural product molecules with similar scaffolds, such as Rocaglamide A (RocA), targeting eIF4A have been reported in the last decade. However, their clinical utilization is still elusive due to several pharmacological limitations. In this study we identified new eIF4A1 inhibitors and their possible mechanisms. METHODS: In this report, we conducted a pharmacophore-based virtual screen of RocA complexed with eIF4A and a polypurine RNA strand for novel eIF4A inhibitors from commercially available compounds in the MolPort Database. We performed target-based screening and optimization of active pharmacophores. We assessed the effects of novel compounds on biochemical and cell-based assays for efficacy and mechanistic evaluation. RESULTS: We validated three new potent eIF4A inhibitors, RBF197, RBF 203, and RBF 208, which decreased diffuse large B-cell lymphoma (DLBCL) cell viability. Biochemical and cellular studies, molecular docking, and functional assays revealed that thosenovel compounds clamp eIF4A into mRNA in an ATP-independent manner. Moreover, we found that RBF197 and RBF208 significantly depressed eIF4A-dependent oncogene expression as well as the colony formation capacity of DLBCL. Interestingly, exposure of these compounds to non-malignant cells had only minimal impact on their growth and viability. CONCLUSIONS: Identified compounds suggest a new strategy for designing novel eIF4A inhibitors.

Systematized analysis of secondary structure dependence of key structural features of residues in soluble and membrane-bound proteins.

Knowledge of three-dimensional protein structure is integral to most modern drug discovery efforts. Recent advancements have highlighted new techniques for 3D protein structure determination and, where structural data cannot be collected experimentally, prediction of protein structure. We have undertaken a major effort to use existing protein structures to collect, characterize, and catalogue the inter-atomic interactions that define and compose 3D structure by mapping hydropathic interaction environments as maps in 3D space. This work has been performed on a residue-by-residue basis, where we have seen evidence for relationships between environment character, residue solvent-accessible surface areas and their secondary structures. In this graphical review, we apply principles from our earlier studies and expand the scope to all common amino acid residue types in both soluble and membrane proteins. Key to this analysis is parsing the Ramachandran plot to an 8-by-8 chessboard to define secondary structure bins. Our analysis yielded a number of quantitative discoveries: 1) increased fraction of hydrophobic residues (alanine, isoleucine, leucine, phenylalanine and valine) in membrane proteins compared to their fractions in soluble proteins; 2) less burial coupled with significant increases in favorable hydrophobic interactions for hydrophobic residues in membrane proteins compared to soluble proteins; and 3) higher burial and more favorable polar interactions for polar residues now preferring the interior of membrane proteins. These observations and the supporting data should provide benchmarks for current studies of protein residues in different environments and may be able to guide future protein structure prediction efforts.

3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps.

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

Synthesis of novel 5-substituted-2-aminotetralin analogs: 5-HT1A and 5-HT7 G protein-coupled receptor affinity, 3D-QSAR and molecular modeling.

The serotonin 5-HT7 G protein-coupled receptor (GPCR) is a proposed pharmacotherapeutic target for a variety of central and peripheral indications, albeit, there are no approved drugs selective for binding 5-HT7. We previously reported that a lead analog based on the 5-substituted-N,N-disubstituted-1,2,3,4-tetrahydronaphthalen-2-amine (5-substituted-2-aminotetralin, 5-SAT) scaffold binds with high affinity at the 5-HT7 GPCR, and can treat symptoms of autism in mouse models; subsequently, the lead was found to have high affinity at the 5-HT1A GPCR. Herein, we report the synthesis of novel 5-SAT analogs to develop a 3-dimensional quantitative structure-affinity relationship (3D-QSAR) at the human 5-HT7 receptor for comparison with similar studies at the highly homologous 5-HT1A receptor. We report 35 new 5-SAT ligands, some with very high affinity (Ki ≤ 1 nM) and stereoselectivity at 5-HT7 + or 5-HT1A receptors, several with modest selectivity (up to 12-fold) for binding at 5-HT7, and, several ligands with high selectivity (up to 40-fold) at the 5-HT1A receptor. 3D-QSAR results indicate that steric extensions at the C(5)-position improve selectivity for the 5-HT7 over 5-HT1A receptor, while steric and hydrophobic extensions at the chiral C(2)-amino position impart 5-HT1A selectivity. In silico receptor homology modeling studies, supplemented with molecular dynamics simulations and binding free energy calculations, were used to rationalize experimentally-determined receptor selectivity and stereoselective affinity results. The data from these studies indicate that the 5-SAT chemotype, previously shown to be safe and efficacious in rodent paradigms of neurodevelopmental and neuropsychiatric disorders, is amenable to structural modification to optimize affinity at serotonin 5-HT7 vs. 5-HT1A GPCRs, as may be required for successful clinical translation.