Recent applications of computational methods to allosteric drug discovery.

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function.

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges.

Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function.

Selective, Small-Molecule Co-Factor Binding Site Inhibition of a Su(var)3-9, Enhancer of Zeste, Trithorax Domain Containing Lysine Methyltransferase.

The first chemical probe to primarily occupy the co-factor binding site of a Su(var)3-9, enhancer of a zeste, trithorax (SET) domain containing protein lysine methyltransferase (PKMT) is reported. Protein methyltransferases require -adenosylmethionine (SAM) as a co-factor (methyl donor) for enzymatic activity. However, SAM itself represents a poor medicinal chemistry starting point for a selective, cell-active inhibitor given its extreme physicochemical properties and its role in multiple cellular processes.

Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain.

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinase's solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales.

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter.

Two-pore domain potassium (K2P) channel ion conductance is regulated by diverse stimuli that directly or indirectly gate the channel selectivity filter (SF). Recent crystal structures for the TREK-2 member of the K2P family reveal distinct "up" and "down" states assumed during activation via mechanical stretch. We performed 195 μs of all-atom, unbiased molecular dynamics simulations of the TREK-2 channel to probe how membrane stretch regulates the SF gate.

Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches.

Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design.

Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches.

The binding affinities (IC) reported for diverse structural and chemical classes of human β-secretase 1 (BACE-1) inhibitors in literature were modeled using multiple in silico ligand based modeling approaches and statistical techniques. The descriptor space encompasses simple binary molecular fingerprint, one- and two-dimensional constitutional, physicochemical, and topological descriptors, and sophisticated three-dimensional molecular fields that require appropriate structural alignments of varied chemical scaffolds in one universal chemical space. The affinities were modeled using qualitative classification or quantitative regression schemes involving linear, nonlinear, and deep neural network (DNN) machine-learning methods used in the scientific literature for quantitative-structure activity relationships (QSAR).

Recent developments in targeting protein misfolding diseases.

Protein misfolding is an emerging field that crosses multiple therapeutic areas and causes many serious diseases. As the biological pathways of protein misfolding become more clearly elucidated, small molecule approaches in this arena are gaining increased attention. This manuscript will survey current small molecules from the literature that are known to modulate misfolding, stabilization or proteostasis.

Small molecules that target protein misfolding.

Protein misfolding is a process in which proteins are unable to attain or maintain their biologically active conformation. Factors contributing to protein misfolding include missense mutations and intracellular factors such as pH changes, oxidative stress, or metal ions. Protein misfolding is linked to a large number of diseases such as cystic fibrosis, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and less familiar diseases such as Gaucher's disease, nephrogenic diabetes insipidus, and Creutzfeldt-Jakob disease.

Discovery of indazoles as inhibitors of Tpl2 kinase.

Synthesis, modeling and structure-activity relationship of indazoles as inhibitors of Tpl2 kinase are described. From a high throughput screening effort, we identified an indazole hit compound 5 that has a single digit micromolar Tpl2 activity. Through SAR modifications at the C3 and C5 positions of the indazole, we discovered compound 31 with good potency in LANCE assay and cell-based p-Erk assay.

Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.

Due to the high attrition rate of central nervous system drug candidates during clinical trials, the assessment of blood-brain barrier (BBB) penetration in early research is particularly important. A genetic approximation (GA)-based regression model was developed for predicting in vivo blood-brain partitioning data, expressed as logBB (log[brain]/[blood]). The model was built using an in-house data set of 193 compounds assembled from 22 different therapeutic projects.

An enriched structural kinase database to enable kinome-wide structure-based analyses and drug discovery.

The development of a kinase structural database, the kinase knowledge base (KKB), is described. It covers all human kinase domain structures that have been deposited in the Protein Data Bank. All structures are renumbered using a common scheme, which enables efficient cross-comparisons and multiple queries of interest to the kinase field.

A structural informatics approach to mine kinase knowledge bases.

In this paper, we describe a combination of structural informatics approaches developed to mine data extracted from existing structure knowledge bases (Protein Data Bank and the GVK database) with a focus on kinase ATP-binding site data. In contrast to existing systems that retrieve and analyze protein structures, our techniques are centered on a database of ligand-bound geometries in relation to residues lining the binding site and transparent access to ligand-based SAR data. We illustrate the systems in the context of the Abelson kinase and related inhibitor structures.

Tetrahydrocarbazole-based serotonin reuptake inhibitor/dopamine D2 partial agonists for the potential treatment of schizophrenia.

A 5-fluoro-tetrahydrocarbazole serotonin reuptake inhibitor (SRI) building block was combined with a variety of linkers and dopamine D2 receptor ligands in an attempt to identify potent D2 partial agonist/SRI molecules for treatment of schizophrenia. This approach has the potential to treat a broader range of symptoms compared to existing therapies. Selected compounds in this series demonstrate high affinity for both targets and D2 partial agonism in cell-based and in vivo assays.