Biology subjects

Bourne, P. E.

Publications and source records attributed to Bourne, P. E..

8 recordsLinked to original sources

bioRxiv2023

Rigid Scaffolds are Promising for Designing Macrocyclic Kinase Inhibitors

Macrocyclic kinase inhibitors (MKIs) are gaining attention due to their favorable selectivity and potential to overcome drug resistance, yet they remain challenging to design because of their novel structures. To facilitate the design and discovery of MKIs, we investigate MKI rational design starting from initial acyclic compounds by performing microsecond-scale atomistic simulations for multiple MKIs, constructing an MKI database, and analyzing MKIs using hierarchical cluster analysis. Our studies demonstrate that the binding modes of MKIs are like that of their corresponding acyclic counterparts against the same kinase targets. Importantly, within the respective binding sites, the MKI scaffolds retain the same conformations as their corresponding acyclic counterparts, demonstrating the rigidity of scaffolds before and after molecular cyclization. The MKI database includes 641 nanomole-level MKIs from 56 human kinases elucidating the features of rigid scaffolds, and the tendency of core structures among MKIs. Collectively these results and resources can facilitate MKI development.

bioinformatics↗

bioRxiv2023

Systematic Analysis of KRAS-ligand Interaction Modes and Flexibilities Reveals the Binding Characteristics

KRAS, a common human oncogene, has been recognized as a critical drug target in treating multiple cancers. After four decades of effort, one allosteric KRAS drug (Sotorasib) has been approved, inspiring more KRAS-targeted drug research. Here we provide the features of KRAS binding pockets and ligand-binding characteristics of KRAS complexes using a structural systems pharmacology approach. Three distinct binding sites (conserved nucleotide-binding site, shallow Switch-I/II pocket, and allosteric Switch-II/3 pocket) are characterized. Ligand-binding features are determined based on encoded KRAS-inhibitor interaction fingerprints. Finally, the flexibility of the three distinct binding sites to accommodate different potential ligands, based on MD simulation, is discussed. Collectively, these findings are intended to facilitate rational KRAS drug design.

bioinformatics↗

bioRxiv2022

Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data

BackgroundMachine learning (ML) has a rich history in structural bioinformatics, and modern approaches, such as deep learning, are revolutionizing our knowledge of the subtle relationships between biomolecular sequence, structure, function, dynamics and evolution. As with any advance that rests upon statistical learning approaches, the recent progress in biomolecular sciences is enabled by the availability of vast volumes of sufficiently-variable data. To be useful, such data must be well-structured, machine-readable, intelligible and manipulable. These and related requirements pose challenges that become especially acute at the computational scales typical in ML. Furthermore, in structural bioinformatics such data generally relate to protein three-dimensional (3D) structures, which are inherently more complex than sequence-based data. A significant and recurring challenge concerns the creation of large, high-quality, openly-accessible datasets that can be used for specific training and benchmarking tasks in ML pipelines for predictive modeling projects, along with reproducible splits for training and testing. ResultsHere, we report Prop3D, a platform that allows for the creation, sharing and extensible reuse of libraries of protein domains, featurized with biophysical and evolutionary properties that can range from detailed, atomically-resolved physicochemical quantities (e.g., electrostatics) to coarser, residue-level features (e.g., phylogenetic conservation). As a community resource, we also supply a Prop3D-20sf protein dataset, obtained by applying our approach to CATH. We have developed and deployed the Prop3D framework, both in the cloud and on local HPC resources, to systematically and reproducibly create comprehensive datasets via the Highly Scalable Data Service (HSDS). Our datasets are freely accessible via a public HSDS instance, or they can be used with accompanying Python wrappers for popular ML frameworks. ConclusionProp3D and its associated Prop3D-20sf dataset can be of broad utility in at least three ways. Firstly, the Prop3D workflow code can be customized and deployed on various cloud-based compute platforms, with scalability achieved largely by saving the results to distributed HDF5 files via HSDS. Secondly, the linked Prop3D-20sf dataset provides a hand-crafted, already-featurized dataset of protein domains for 20 highly-populated CATH families; importantly, provision of this pre-computed resource can aid the more efficient development (and reproducible deployment) of ML pipelines. Thirdly, Prop3D-20sfs construction explicitly takes into account (in creating datasets and data-splits) the enigma of data leakage, stemming from the evolutionary relationships between proteins.

bioinformatics↗

bioRxiv2022

Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe

Modern proteins did not arise abruptly, as singular events, but rather over the course of at least 3.5 billion years of evolution. Can machine learning teach us how this occurred? The molecular evolutionary processes that yielded the intricate three-dimensional (3D) structures of proteins involve duplication, recombination and mutation of genetic elements, corresponding to short peptide fragments. Identifying and elucidating these ancestral fragments is crucial to deciphering the interrelationships amongst proteins, as well as how evolution acts upon protein sequences, structures & functions. Traditionally, structural fragments have been found using sequence-based and 3D structural alignment approaches, but that becomes challenging when proteins have undergone extensive permutations--allowing two proteins to share a common architecture, though their topologies may drastically differ (a phenomenon termed the Urfold). We have designed a new framework to identify compact, potentially-discontinuous peptide fragments by combining (i) deep generative models of protein superfamilies with (ii) layerwise relevance propagation (LRP) to identify atoms of great relevance in creating an embedding during an allsuperfamilies x alldomains analysis. Our approach recapitulates known relationships amongst the evolutionarily ancient small {beta}-barrels (e.g. SH3 and OB folds) and amongst P-loop-containing proteins (e.g. Rossmann and P-loop NTPases), previously established via manual analysis. Because of the generality of our deep models approach, we anticipate that it can enable the discovery of new ancestral peptides. In a sense, our framework uses LRP as an explainable AI approach, in conjunction with a recent deep generative model of protein structure (termed DeepUrfold), in order to leverage decades worth of structural biology knowledge to decipher the underlying molecular bases for protein structural relationships--including those which are exceedingly remote, yet discoverable via deep learning.

bioinformatics↗

bioRxiv2022

Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/ continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfolds learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a new, mostly-continuous view of fold space--a view that extends beyond simple geometric similarity, towards the realm of integrated sequence{leftrightarrow}structure{leftrightarrow}function properties.

bioinformatics↗

bioRxiv2019

Revealing acquired resistance mechanisms of kinase-targeted drugs using an on-the-fly, function-site interaction fingerprint approach

Although kinase-targeted drugs have achieved significant clinical success, they are frequently subject to the limitations of drug resistance, which has become a primary vulnerability to targeted drug therapy. Therefore, deciphering resistance mechanisms is an important step in designing more efficacious, anti-resistant, drugs. Here we studied two FDA-approved kinase drugs: Crizotinib and Ceritinib, which are first- and second-generation anaplastic lymphoma kinase (ALK) targeted inhibitors, to unravel drug-resistance mechanisms. We used an on-the-fly, function-site interaction fingerprint (on-the-fly Fs-IFP) approach by combining binding free energy surface calculations with the Fs-IFPs. Establishing the potentials of mean force and monitoring the atomic-scale protein-ligand interactions, before and after the L1196M-induced drug resistance, revealed insights into drug-resistance/anti-resistant mechanisms. Crizotinib prefers to bind the wild type ALK kinase domain, whereas Ceritinib binds more favorably to the mutated ALK kinase domain, in agreement with experimental results. We determined that ALK kinase-drug interactions in the region of the front pocket are associated with drug resistance. Additionally, we find that the L1196M mutation does not simply alter the binding modes of inhibitors, but also affects the flexibility of the entire ALK kinase domain. Our work provides an understanding of the mechanisms of ALK drug resistance, confirms the usefulness of the on-the-fly Fs-IFP approach and provides a practical paradigm to study drug-resistance mechanisms in prospective drug discovery.

biochemistry↗

bioRxiv2019

The Urfold: Structural Similarity Just above the Superfold Level?

OverviewWe suspect that there is a level of granularity of protein structure intermediate between the classical levels of architecture and topology, as reflected in such phenomena as extensive 3D structural similarity above the level of (super)folds. Here, we examine this notion of architectural identity despite topological variability, starting with a concept that we call the Urfold. We believe that this model could offer a new conceptual approach for protein structural analysis and classification: indeed, the Urfold concept may help reconcile various phenomena that have been frequently recognized or debated for years, such as the precise meaning of significant structural overlap and the degree of continuity of fold space. More broadly, the role of structural similarity in sequence/structure/function evolution has been studied via many models over the years; the Urfold may help synthesize these models into a generalized, consistent framework, by addressing a conceptual gap that we believe exists between the architecture and topology levels of structural classification schemes.

bioinformatics↗

bioRxiv2017

The Structure Of Small Beta Barrels

0.The small {beta}-barrel is an ancient protein structural domain characterized by extremes: It features an extremely broad range of structural varieties, a deeply intricate evolutionary history, and it is associated with a bewildering array of biomolecular pathways and physiological functions. These and related features of this domain are described and analyzed herein. Specifically, we present a comprehensive, survey-based analysis of the structural properties of small {beta}-barrels (SBBs). We first consider the defining characteristics of the SBB fold, as well as the various systems of nomenclature used to describe it. In order to begin elucidating how such vast functional diversity is achieved by a relatively simple protein domain, we then explore the anatomy of the SBB fold and some of its representative structural variants. Many types of SBB proteins assemble into cyclic oligomers that act as the biologically-functional entity. These oligomers exhibit a great deal of plasticity even at the quaternary structural level--including homomeric and heteromeric assemblies, rings of variable subunit stoichiometries (pentamer, hexamer, etc.), as well as higher-order oligomers (e.g., double-rings) and fibrillar polymers. We conclude with three themes that emerge from the SBBs unique structure{leftrightarrow}function versatility.

bioinformatics↗