Extended Graph-Based Models for Enhanced Similarity Search in Cavbase

Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening

While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces

sensaas is a tool developed for aligning and comparing molecular shapes and sub-shapes. Alignment is obtained by registration of 3D point-based representations of the van der Waals surface. The method uses local properties of the shape to identify the correspondence relationships between two point clouds containing up to several thousand colored (labeled) points. Our rigid-body superimposition method follows a two-stage approach. An initial alignment is obtained by matching pose-invariant local 3D descriptors, called FPFH, of the input point clouds. This stage provides a global superimposition of the molecular surfaces, without any knowledge of their initial pose in 3D space. This alignment is then refined by optimizing the matching of colored points. In our study, each point is colored according to its closest atom, which itself belongs to a user defined physico-chemical class. Finally, sensaas provides an alignment and evaluates the molecular similarity by using Tversky coefficients. To assess the efficiency of this approach, we tested its ability to reproduce the superimposition of X-ray structures of the benchmarking AstraZeneca (AZ) data set and, compared its results with those generated by the two shape-alignment approaches shaep and shafts. We also illustrated submatching properties of our method with respect to few substructures and bioisosteric fragments. The code is available upon request from the authors (demo version at https://chemoinfo.ipmc.cnrs.fr/SENSAAS).

Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

Acceleration of Binding Site Comparisons by Graph Partitioning

The comparison of protein binding sites is a prominent task in computational chemistry and has been studied in many different ways. For the automatic detection and comparison of putative binding cavities the Cavbase system has been developed which uses a coarse-grained set of pseudocenters to represent the physicochemical properties of a binding site and employs a graph-based procedure to calculate similarities between two binding sites. However, the comparison of two graphs is computationally quite demanding which makes large-scale studies such as the rapid screening of entire databases hardly feasible. In a recent work, we proposed the method Local Cliques (LC) for the efficient comparison of Cavbase binding sites. It employs a clique heuristic to detect the maximum common subgraph of two binding sites and an extended graph model to additionally compare the shape of individual surface patches. In this study, we present an alternative to further accelerate the LC method by partitioning the binding-site graphs into disjoint components prior to their comparisons. The pseudocenter sets are split with regard to their assigned phyiscochemical type, which leads to seven much smaller graphs than the original one. Applying this approach on the same test scenarios as in the former comprehensive way results in a significant speed-up without sacrificing accuracy.

Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real

Determination of structural similarities between protein binding pockets is an important challenge in in silico drug design. It can help to understand selectivity considerations, predict unexpected ligand cross-reactivity, and support the putative annotation of function to orphan proteins. To this end, Cavbase was developed as a tool for the automated detection, storage, and classification of putative protein binding sites. In this context, binding sites are characterized as sets of pseudocenters, which denote surface-exposed physicochemical properties, and can be used to enable mutual binding site comparisons. However, these comparisons tend to be computationally very demanding and often lead to very slow computations of the similarity measures. In this study, we propose RAPMAD (RApid Pocket MAtching using Distances), a new evaluation formalism for Cavbase entries that allows for ultrafast similarity comparisons. Protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity. Attaining a speed of more than 20 000 comparisons per second, screenings across large data sets and even entire databases become easily feasible. We demonstrate the discriminative power and the short runtime by performing several classification and retrieval experiments. RAPMAD attains better success rates than the comparison formalism originally implemented into Cavbase or several alternative approaches developed in recent time, while requiring only a fraction of their runtime. The pratical use of our method is finally proven by a successful prospective virtual screening study that aims for the identification of novel inhibitors of the NMDA receptor.

Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity

Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information. In this study, we present the results of a very simplistic and shape-biased comparison approach, which stress that unrestricted cavity extraction is essential to enable unexpected cross-reactivity predictions among proteins and function annotations of orphan proteins.