Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models

Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.

FRAGMENTISE: A user-friendly, cross-platform tool to create and analyze comprehensive small-molecule fragment databases

The ongoing COVID-19 pandemic, and constant demand for new therapies in unmet clinical needs, necessitates strategies to identify drug candidates for rapid clinical deployment. Over the years, fragment-based drug design (FBDD) has emerged as a mainstream lead discovery strategy in academia, biotechnology start-ups, and large pharma. Chemical building block libraries are the fundamental component of virtually any FBDD campaign. Current trends focus on smaller and smarter libraries that offer synthetically amenable starting points for rational lead generation. Therefore, there remains an ever-increasing need for new methods to generate fragment libraries to seed early-stage drug discovery programs. Here, we present FRAGMENTISE-a new user-friendly, cross-platform tool for user-tunable retrosynthetic small-molecule fragmentation. FRAGMENTISE allows for visualization, similarity search, annotation, and in-depth analysis of the fragment databases in the medicinal chemistry context. FRAGMENTISE is available as standalone software for Linux, Windows, and macOS users, with a graphical interface or command-line version.

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

DSR: enhanced modelling and refinement of disordered structures with SHELXL

One of the remaining challenges in single-crystal structure refinement is the proper description of disorder in crystal structures. This paper describes a computer program that performs semi-automatic modelling of disordered moieties in SHELXL [Sheldrick (2015 ▶). Acta Cryst. C71, 3-8.]. The new program contains a database that includes molecular fragments and their corresponding stereochemical restraints, and a placement procedure to place these fragments on the desired position in the unit cell. The program is also suitable for speeding up model building of well ordered crystal structures.

Missing fragments: detecting cooperative binding in fragment-based drug design

The aim of fragment-based drug design (FBDD) is to identify molecular fragments that bind to alternate subsites within a given binding pocket leading to cooperative binding when linked. In this study, the binding of fragments to human phenylethanolamine N-methyltransferase is used to illustrate how (a) current protocols may fail to detect fragments that bind cooperatively, (b) theoretical approaches can be used to validate potential hits, and (c) apparent false positives obtained when screening against cocktails of fragments may in fact indicate promising leads.