Relative timing information and orthology in evolutionary scenarios.

BACKGROUND: Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree T to vertices and edges of a species tree S. The relative timing of the last common ancestors of two extant genes (leaves of T) and the last common ancestors of the two species (leaves of S) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph. RESULTS: Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. While both LDT and PDT graphs are cographs, this is not true for the EDT graph in general. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs.

Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery.

In recent years machine learning has transformed many aspects of the drug discovery process including small molecule design for which the prediction of the bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches, but is fundamentally limited by the accuracy with which protein:ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase:inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures co-crystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the co-crystallized ligand-utilizing shape overlap with or without maximum common substructure matching-are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance to generate a low RMSD docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar co-crystallized ligands according to shape and electrostatics proofed to be the most efficient way to reproduce binding poses achieving a success rate of 66.9 % across all included systems. The studied docking and pose selection strategies-which utilize the OpenEye Toolkit-were implemented into pipelines of the KinoML framework allowing automated and reliable protein:ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe the general findings can also be transferred to other protein families.

Clustering systems of phylogenetic networks.

Rooted acyclic graphs appear naturally when the phylogenetic relationship of a set X of taxa involves not only speciations but also recombination, horizontal transfer, or hybridization that cannot be captured by trees. A variety of classes of such networks have been discussed in the literature, including phylogenetic, level-1, tree-child, tree-based, galled tree, regular, or normal networks as models of different types of evolutionary processes. Clusters arise in models of phylogeny as the sets [Formula: see text] of descendant taxa of a vertex v. The clustering system [Formula: see text] comprising the clusters of a network N conveys key information on N itself. In the special case of rooted phylogenetic trees, T is uniquely determined by its clustering system [Formula: see text]. Although this is no longer true for networks in general, it is of interest to relate properties of N and [Formula: see text]. Here, we systematically investigate the relationships of several well-studied classes of networks and their clustering systems. The main results are correspondences of classes of networks and clustering systems of the following form: If N is a network of type [Formula: see text], then [Formula: see text] satisfies [Formula: see text], and conversely if [Formula: see text] is a clustering system satisfying [Formula: see text] then there is network N of type [Formula: see text] such that [Formula: see text].This, in turn, allows us to investigate the mutual dependencies between the distinct types of networks in much detail.

MEN1 mutations mediate clinical resistance to menin inhibition.

Chromatin-binding proteins are critical regulators of cell state in haematopoiesis1,2. Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene (KMT2Ar) or mutation of the nucleophosmin gene (NPM1) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs3-5. In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin-MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2Ar or mutated NPM1 (ref. 6). Here we identified somatic mutations in MEN1 at the revumenib-menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor-menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug-target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance.

Best Match Graphs With Binary Trees.

Best match graphs (BMG) are a key intermediate in graph-based orthology detection and contain a large amount of information on the gene tree. We provide a near-cubic algorithm to determine whether a BMG is binary-explainable, i.e., whether it can be explained by a fully resolved gene tree and, if so, to construct such a tree. Moreover, we show that all such binary trees are refinements of the unique binary-refinable tree (BRT), which in general is a substantial refinement of the also unique least resolved tree of a BMG. Finally, we show that the problem of editing an arbitrary vertex-colored graph to a binary-explainable BMG is NP-complete and provide an integer linear program formulation for this task.

TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research.

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming-especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

A simpler linear-time algorithm for the common refinement of rooted phylogenetic trees on a common leaf set.

BACKGROUND: The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic. RESULTS: An O(k|L|) algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set L is proposed that explicitly computes the parent function of T in a bottom-up approach. CONCLUSION: LinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons. AVAILABILITY: An implementation of LinCR in Python is freely available at https://github.com/david-schaller/tralda .

Heuristic algorithms for best match graph editing.

BACKGROUND: Best match graphs (BMGs) are a class of colored digraphs that naturally appear in mathematical phylogenetics as a representation of the pairwise most closely related genes among multiple species. An arc connects a gene x with a gene y from another species (vertex color) Y whenever it is one of the phylogenetically closest relatives of x. BMGs can be approximated with the help of similarity measures between gene sequences, albeit not without errors. Empirical estimates thus will usually violate the theoretical properties of BMGs. The corresponding graph editing problem can be used to guide error correction for best match data. Since the arc set modification problems for BMGs are NP-complete, efficient heuristics are needed if BMGs are to be used for the practical analysis of biological sequence data. RESULTS: Since BMGs have a characterization in terms of consistency of a certain set of rooted triples (binary trees on three vertices) defined on the set of genes, we consider heuristics that operate on triple sets. As an alternative, we show that there is a close connection to a set partitioning problem that leads to a class of top-down recursive algorithms that are similar to Aho's supertree algorithm and give rise to BMG editing algorithms that are consistent in the sense that they leave BMGs invariant. Extensive benchmarking shows that community detection algorithms for the partitioning steps perform best for BMG editing. CONCLUSION: Noisy BMG data can be corrected with sufficient accuracy and efficiency to make BMGs an attractive alternative to classical phylogenetic methods.

Indirect identification of horizontal gene transfer.

Several implicit methods to infer horizontal gene transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs.

Garcinol from Garcinia indica inhibits HIV-1 reverse transcriptase-associated ribonuclease H.

The bioactive components of Garcinia indica, garcinol (camboginol), and isogarcinol (cambogin), are suitable drug candidates for the treatment of various human diseases. HIV-1-RNase H assay was used to study the RNase H inhibition by garcinol and isogarcinol. Docking of garcinol into the active site of the enzyme was carried out to rationalize the difference in activities between the two compounds. Garcinol showed higher HIV-1-RNase H inhibition than the known inhibitor RDS1759 and retained full potency against the RNase H of a drug-resistant HIV-1 reverse transcriptase form. Isogarcinol was distinctly less active than garcinol, indicating the importance of the enolizable ß-diketone moiety of garcinol for anti-RNase H activity. Docking calculations confirmed these findings and suggested this moiety to be involved in the chelation of metal ions of the active site. On the basis of its HIV-1 reverse transcriptase-associated RNase H inhibitory activity, garcinol is worth being further explored concerning its potential as a cost-effective treatment for HIV patients.

Corrigendum to "Best match graphs".

Two errors in the article Best Match Graphs (Geiß et al. in JMB 78: 2015-2057, 2019) are corrected. One concerns the tacit assumption that digraphs are sink-free, which has to be added as an additional precondition in Lemma 9, Lemma 11, Theorem 4. Correspondingly, Algorithm 2 requires that its input is sink-free. The second correction concerns an additional necessary condition in Theorem 9 required to characterize best match graphs. The amended results simplify the construction of least resolved trees for n-cBMGs, i.e., Algorithm 1. All other results remain unchanged and are correct as stated.

Exploiting Water Dynamics for Pharmacophore Screening.

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

New 3-Aryl-2-(2-thienyl)acrylonitriles with High Activity Against Hepatoma Cells.

New 2-(thien-2-yl)-acrylonitriles with putative kinase inhibitory activity were prepared and tested for their antineoplastic efficacy in hepatoma models. Four out of the 14 derivatives were shown to inhibit hepatoma cell proliferation at (sub-)micromolar concentrations with IC50 values below that of the clinically relevant multikinase inhibitor sorafenib, which served as a reference. Colony formation assays as well as primary in vivo examinations of hepatoma tumors grown on the chorioallantoic membrane of fertilized chicken eggs (CAM assay) confirmed the excellent antineoplastic efficacy of the new derivatives. Their mode of action included an induction of apoptotic capsase-3 activity, while no contribution of unspecific cytotoxic effects was observed in LDH-release measurements. Kinase profiling of cancer relevant protein kinases identified the two 3-aryl-2-(thien-2-yl)acrylonitrile derivatives 1b and 1c as (multi-)kinase inhibitors with a preferential activity against the VEGFR-2 tyrosine kinase. Additional bioinformatic analysis of the VEGFR-2 binding modes by docking and molecular dynamics calculations supported the experimental findings and indicated that the hydroxy group of 1c might be crucial for its distinct inhibitory potency against VEGFR-2. Forthcoming studies will further unveil the underlying mode of action of the promising new derivatives as well as their suitability as an urgently needed novel approach in HCC treatment.

Complete Characterization of Incorrect Orthology Assignments in Best Match Graphs.

Genome-scale orthology assignments are usually based on reciprocal best matches. In the absence of horizontal gene transfer (HGT), every pair of orthologs forms a reciprocal best match. Incorrect orthology assignments therefore are always false positives in the reciprocal best match graph. We consider duplication/loss scenarios and characterize unambiguous false-positive (u-fp) orthology assignments, that is, edges in the best match graphs (BMGs) that cannot correspond to orthologs for any gene tree that explains the BMG. Moreover, we provide a polynomial-time algorithm to identify all u-fp orthology assignments in a BMG. Simulations show that at least [Formula: see text] of all incorrect orthology assignments can be detected in this manner. All results rely only on the structure of the BMGs and not on any a priori knowledge about underlying gene or species trees.

From Pyrazolones to Azaindoles: Evolution of Active-Site SHP2 Inhibitors Based on Scaffold Hopping and Bioisosteric Replacement.

The tyrosine phosphatase SHP2 controls the activity of pivotal signaling pathways, including MAPK, JAK-STAT, and PI3K-Akt. Aberrant SHP2 activity leads to uncontrolled cell proliferation, tumorigenesis, and metastasis. SHP2 signaling was recently linked to drug resistance against cancer medications such as MEK and BRAF inhibitors. In this work, we present the development of a novel class of azaindole SHP2 inhibitors. We applied scaffold hopping and bioisosteric replacement concepts to eliminate unwanted structural motifs and to improve the inhibitor characteristics of the previously reported pyrazolone SHP2 inhibitors. The most potent azaindole 45 inhibits SHP2 with an IC50 = 0.031 µM in an enzymatic assay and with an IC50 = 2.6 µM in human pancreas cells (HPAF-II). Evaluation in a series of cellular assays for metastasis and drug resistance demonstrated efficient SHP2 blockade. Finally, 45 inhibited proliferation of two cancer cell lines that are resistant to cancer drugs and diminished ERK signaling.

PyRod Enables Rational Homology Model-based Virtual Screening Against MCHR1.

Several encouraging pre-clinical results highlight the melanin-concentrating hormone receptor 1 (MCHR1) as promising target for anti-obesity drug development. Currently however, experimentally resolved structures of MCHR1 are not available, which complicates rational drug design campaigns. In this study, we aimed at developing accurate, homologymodel-based 3D pharmacophores against MCHR1. We show that traditional approaches involving docking of known active small molecules are hindered by the flexibility of binding pocket residues. Instead, we derived three-dimensional pharmacophores from molecular dynamics simulations by employing our novel open-source software PyRod. In a retrospective evaluation, the generated 3D pharmacophores were highly predictive returning up to 35 % of active molecules and showing an early enrichment (EF1) of up to 27.6. Furthermore, PyRod pharmacophores demonstrate higher sensitivity than ligand-based pharmacophores and deliver structural insights, which are key to rational lead optimization.

Catching a Moving Target: Comparative Modeling of Flaviviral NS2B-NS3 Reveals Small Molecule Zika Protease Inhibitors.

The pivotal role of viral proteases in virus replication has already been successfully exploited in several antiviral drug design campaigns. However, no efficient antivirals are currently available against flaviviral infections. In this study, we present lead-like small molecule inhibitors of the Zika Virus (ZIKV) NS2B-NS3 protease. Since only few nonpeptide competitive ligands are known, we take advantage of the high structural similarity with the West Nile Virus (WNV) NS2B-NS3 protease. A comparative modeling approach involving our in-house software PyRod was employed to systematically analyze the binding sites and develop molecular dynamics-based 3D pharmacophores for virtual screening. The identified compounds were biochemically characterized revealing low micromolar affinity for both ZIKV and WNV proteases. Their lead-like properties together with rationalized binding modes represent valuable starting points for future lead optimization. Since the NS2B-NS3 protease is highly conserved among flaviviruses, these compounds may also drive the development of pan-flaviviral antiviral drugs.

From pairs of most similar sequences to phylogenetic best matches.

BACKGROUND: Many of the commonly used methods for orthology detection start from mutually most similar pairs of genes (reciprocal best hits) as an approximation for evolutionary most closely related pairs of genes (reciprocal best matches). This approximation of best matches by best hits becomes exact for ultrametric dissimilarities, i.e., under the Molecular Clock Hypothesis. It fails, however, whenever there are large lineage specific rate variations among paralogous genes. In practice, this introduces a high level of noise into the input data for best-hit-based orthology detection methods. RESULTS: If additive distances between genes are known, then evolutionary most closely related pairs can be identified by considering certain quartets of genes provided that in each quartet the outgroup relative to the remaining three genes is known. A priori knowledge of underlying species phylogeny greatly facilitates the identification of the required outgroup. Although the workflow remains a heuristic since the correct outgroup cannot be determined reliably in all cases, simulations with lineage specific biases and rate asymmetries show that nearly perfect results can be achieved. In a realistic setting, where distances data have to be estimated from sequence data and hence are noisy, it is still possible to obtain highly accurate sets of best matches. CONCLUSION: Improvements of tree-free orthology assessment methods can be expected from a combination of the accurate inference of best matches reported here and recent mathematical advances in the understanding of (reciprocal) best match graphs and orthology relations. AVAILABILITY: Accompanying software is available at https://github.com/david-schaller/AsymmeTree.

Discovery of Michael acceptor containing 1,4-dihydropyridines as first covalent inhibitors of L-/T-type calcium channels.

1,4-Dihydropyridines (DHPs) are an important class of blockers targeting different calcium channel subtypes and have great therapeutic value against cardiovascular and neurophysiologic conditions. Here, we present the design of DHP-based hexahydroquinoline derivatives as either selective or covalent inhibitors of calcium channels. These compounds were synthesized via a modified Hantzsch reaction under microwave irradiation and characterized by IR, 1H NMR, 13C NMR and mass spectra. Additionally, the proposed structure of HM12 was resolved by single crystal X-ray analysis. The abilities of the target compounds to block both L- and T-type calcium channels were evaluated by utilizing the whole-cell patch clamp technique. Our results identified covalent inhibitors of calcium channels for the first time, which could be achieved by introducing a Michael acceptor group into the ester side chain of the compounds. The proposed covalent binding between the compounds and the cysteine amino acid (Cys1492) within the DHP binding pocket of L-type calcium channel was supported by docking and pharmacophore analysis as well as a glutathione reactivity assay.

Ligand-guided homology modeling drives identification of novel histamine H3 receptor ligands.

In this study, we report a ligand-guided homology modeling approach allowing the analysis of relevant binding site residue conformations and the identification of two novel histamine H3 receptor ligands with binding affinity in the nanomolar range. The newly developed method is based on exploiting an essential charge interaction characteristic for aminergic G-protein coupled receptors for ranking 3D receptor models appropriate for the discovery of novel compounds through virtual screening.