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Hugo
Macdermott-Opeskin
,
Jenke
Scheen
,
Cas
Wognum
,
Joshua T.
Horton
,
Devany
West
,
Alexander M.
Payne
,
Maria A.
Castellanos
,
Sean
Colby
,
Edward
Griffen
,
David
Cousins
,
Jessica
Stacey
,
Lauren
Reid
,
Jasmin Cara
Aschenbrenner
,
Daren
Fearon
,
Blake H.
Balcomb
,
Peter
Marples
,
Charles W. E.
Tomlinson
,
Ryan
Lithgo
,
Andre S.
Godoy
,
Max
Winokan
,
Noa
Lahav
,
Shirley
Duberstein
,
Honore
Etsmoberg
,
Lu
Zhu
,
Andrew
Quirke
,
Mohamed Iliyas
Abdul Haleem
,
Irfan
Alibay
,
Gunjan
Baid
,
Benjamin
Birnbaum
,
Kevin P.
Bishop
,
Hugo
Bohorquez
,
Ashmita
Bose
,
C. J.
Brown
,
Jackson
Burns
,
Lianjin
Cai
,
Ruel
Cedeno
,
Stephane
De Cesco
,
Vladimir
Chupakhin
,
Finlay
Clark
,
Daniel J.
Cole
,
Carles
Corbi-Verge
,
Muhammad
Danial
,
Alec
Davi
,
Wim
Dehaen
,
Niklas Piet
Doering
,
Alexis
Dougha
,
Marie-Pierre
Dréanic
,
Bryce
Eakin
,
Anatol
Ehrlich
,
Rokas
Elijosius
,
Jozef
Fülöp
,
Anthony
Gitter
,
Kenneth
Goossens
,
Yaowen
Gu
,
Teresa
Head-Gordon
,
Laurent
Hoffer
,
Johan
Hofmans
,
Ellena
Jiang
,
Benjamin
Kaminow
,
Sina
Khosravi
,
Asma Feriel
Khoualdi
,
Eelke Bart
Lenselink
,
Zhirong
Liu
,
Yue
Liu
,
Sijie
Liu
,
Yizhou
Ma
,
Patrick
Maher
,
Imke
Mayer
,
Oscar
Mendez-Lucio
,
Antonia S. J. S.
Mey
,
Julien
Michel
,
Floriane
Montanari
,
Taoyu
Niu
,
Ryusei
Ogino
,
Ashok
Palaniappan
,
Xiaolin
Pan
,
Auro
Patnaik
,
Long-Hung
Pham
,
Luis
Pinto
,
Justin
Purnomo
,
Alex
Rich
,
Lars
Schaaf
,
Christoph
Schran
,
Rajeev Kumar
Singh
,
Mounika
Srilakshmi
,
Satya Pratik
Srivastava
,
Kunyang
Sun
,
Zhaoxi
Sun
,
Valerij
Talagayev
,
Balamurugan
Thirukonda Subramanian Balakrishnan
,
Ida
Titus
,
Alexandre
Tkatchenko
,
Wojtek
Treyde
,
Giovanni
Tricarico
,
Austin
Tripp
,
Nopsinth
Vithayapalert
,
Yingze
Wang
,
Azmine Toushik
Wasi
,
Steffen
Wedig
,
Gerhard
Wolber
,
Bofei
Xu
,
Weijun
Zhou
,
Frank
Von Delft
,
John D.
Chodera
Abstract: Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay end points, using previously undisclosed comprehensive experimental drug discovery data sets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top-performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.
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Feb 2026
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B21-High Throughput SAXS
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Diamond Proposal Number(s):
[30465]
Open Access
Abstract: The interaction between the RAD51 and BRCA2 proteins is central to homologous recombination (HR), a crucial pathway ensuring high-fidelity DNA repair. Recruitment of RAD51 involves eight highly conserved regions on BRCA2, named BRC repeats. The interaction between the fourth BRC repeat (BRC4) and the RAD51 C-terminal domain has been structurally characterized, while the complex of full-length RAD51 with the peptide remains elusive. This gap limits our understanding of cytosolic RAD51 recruitment driven by the BRCA2 BRC repeats, which is one of the first crucial steps in HR. Here, we report an integrative experimental and in silico approach to reconstruct the conformational ensemble in solution for full-length RAD51 in complex with BRC4. We combined AlphaFold2, cross-linking mass spectrometry, and small-angle X-ray scattering data with molecular dynamics simulations. Our results show that the full-length RAD51–BRC4 complex is a mixture of compact and elongated conformations and allow for the identification of key residues at the interface between the RAD51 N-terminus and BRC4, mediating complex conformational dynamics. Our evidence provides robust atomic-level insights into the RAD51–BRC4 interaction, shedding light on the molecular features that govern the recognition between these two proteins, while unveiling novel hotspots for developing novel anticancer agents.
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Oct 2025
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NONE-No attached Diamond beamline
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Abstract: The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment–lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein–ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.
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Mar 2024
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Jack
Scantlebury
,
Lucy
Vost
,
Anna
Carbery
,
Thomas E.
Hadfield
,
Oliver M.
Turnbull
,
Nathan
Brown
,
Vijil
Chenthamarakshan
,
Payel
Das
,
Harold
Grosjean
,
Frank
Von Delft
,
Charlotte M.
Deane
Open Access
Abstract: Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.
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May 2023
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Open Access
Abstract: Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment–protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC50 values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.
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May 2023
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I03-Macromolecular Crystallography
I04-1-Macromolecular Crystallography (fixed wavelength)
I04-Macromolecular Crystallography
|
Diamond Proposal Number(s):
[14636, 17183]
Open Access
Abstract: Fragment-based drug discovery has led to six approved drugs, but the small sizes of the chemical fragments used in such methods typically result in only weak interactions between the fragment and its target molecule, which makes it challenging to experimentally determine the three-dimensional poses fragments assume in the bound state. One computational approach that could help address this difficulty is long-timescale molecular dynamics (MD) simulations, which have been used in retrospective studies to recover experimentally known binding poses of fragments. Here, we present the results of long-timescale MD simulations that we used to prospectively discover binding poses for two series of fragments in allosteric pockets on a difficult and important pharmaceutical target, protein tyrosine phosphatase 1b (PTP1b). Our simulations reversibly sampled the fragment association and dissociation process. One of the binding pockets found in the simulations has not to our knowledge been previously observed with a bound fragment, and the other pocket adopted a very rare conformation. We subsequently obtained high-resolution crystal structures of members of each fragment series bound to PTP1b, and the experimentally observed poses confirmed the simulation results. To the best of our knowledge, our findings provide the first demonstration that MD simulations can be used prospectively to determine fragment binding poses to previously unidentified pockets.
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Apr 2023
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Luiz Carlos
Saramago
,
Marcos V.
Santana
,
Bárbara Figueira
Gomes
,
Rafael Ferreira
Dantas
,
Mario R.
Senger
,
Pedro Henrique
Oliveira Borges
,
Vivian Neuza
Dos Santos Ferreira
,
Alice
Dos Santos Rosa
,
Amanda Resende
Tucci
,
Milene
Dias Miranda
,
Petra
Lukacik
,
Claire
Strain-Damerell
,
C. David
Owen
,
Martin A.
Walsh
,
Sabrina
Baptista Ferreira
,
Floriano Paes
Silva-Junior
Abstract: SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp’s, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 μM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.
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Apr 2023
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I04-Macromolecular Crystallography
|
Dušan
Petrović
,
James S.
Scott
,
Michael S.
Bodnarchuk
,
Olivier
Lorthioir
,
Scott
Boyd
,
George M.
Hughes
,
Jordan
Lane
,
Allan
Wu
,
David
Hargreaves
,
James
Robinson
,
Jens
Sadowski
Abstract: ROS1 rearrangements account for 1–2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.
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Aug 2022
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Open Access
Abstract: Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.
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Jan 2022
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I24-Microfocus Macromolecular Crystallography
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Diamond Proposal Number(s):
[17212]
Abstract: Widespread bacterial resistance to carbapenem antibiotics is an increasing global health concern. Resistance has emerged due to carbapenem-hydrolyzing enzymes, including metallo-β-lactamases (MβLs), but despite their prevalence and clinical importance, MβL mechanisms are still not fully understood. Carbapenem hydrolysis by MβLs can yield alternative product tautomers with the potential to access different binding modes. Here, we show that a combined approach employing crystallography and quantum mechanics/molecular mechanics (QM/MM) simulations allow tautomer assignment in MβL:hydrolyzed antibiotic complexes. Molecular simulations also examine (meta)stable species of alternative protonation and tautomeric states, providing mechanistic insights into β-lactam hydrolysis. We report the crystal structure of the hydrolyzed carbapenem ertapenem bound to the L1 MβL from Stenotrophomonas maltophilia and model alternative tautomeric and protonation states of both hydrolyzed ertapenem and faropenem (a related penem antibiotic), which display different binding modes with L1. We show how the structures of both complexed β-lactams are best described as the (2S)-imine tautomer with the carboxylate formed after β-lactam ring cleavage deprotonated. Simulations show that enamine tautomer complexes are significantly less stable (e.g., showing partial loss of interactions with the L1 binuclear zinc center) and not consistent with experimental data. Strong interactions of Tyr32 and one zinc ion (Zn1) with ertapenem prevent a C6 group rotation, explaining the different binding modes of the two β-lactams. Our findings establish the relative stability of different hydrolyzed (carba)penem forms in the L1 active site and identify interactions important to stable complex formation, information that should assist inhibitor design for this important antibiotic resistance determinant.
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Oct 2021
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