AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology

Goßen, Jonas; Ribeiro, Rui P.P.L.; Bier, Dirk; Neumaier, Bernd; Carloni, Paolo; Giorgetti, Alejandro; Rossetti, Giulia

doi:10.1039/d3sc02352d

Cited by 3 publications

(2 citation statements)

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“…We can explore the driving forces for many essential processes performed by large assembled structures that are workhorses in biological systems. Prototype systems of this kind include the nuclear pore complex, the ribosome, , large membrane protein complexes, microtubules, and viruses . Outside of the realm of molecular-level biophysical science, how these large complexes guide the coupled interactions in systems biology will be a further horizon that will begin to come into focus.…”

Section: Discussionmentioning

confidence: 99%

“…Highly diverse, complementary, and cutting-edge topics included conformational sampling, , protein thermodynamics and kinetics, drug discovery, , biomachines, large macromolecular complexes such as the nuclear pore complex and the ribosome, enzyme catalysis by scalable QM/MM molecular dynamics algorithms, AI/ML techniques for bioinformatics and simulation, and coarse graining approaches . In addition, several speakers from Jülich, Oak Ridge, and elsewhere, rooted in computer science and software engineering, discussed the core computational challenges and opportunities related to exascale computing in biology. , …”

Section: Introductionmentioning

confidence: 99%

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All-Atom Biomolecular Simulation in the Exascale Era

Beck,

Carloni,

Asthagiri

2024

J. Chem. Theory Comput.

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Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

All-Atom Biomolecular Simulation in the Exascale Era

Beck,

Carloni,

Asthagiri

2024

J. Chem. Theory Comput.

Self Cite

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Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Frasnetti,

Magni,

Castelli

et al. 2024

Current Opinion in Structural Biology

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Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)

Karakoltzidis,

Karakitsios,

Sarigiannis

2024

Preprint

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The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Moreover, wet lab experimentation is associated with particularly high costs, does not adhere to the principle of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalyzed reaction to determine Michaelis–Menten maximal velocities (Vmax). The molecular fingerprints employed include RCDK standard fingerprints (1024 bits), MACCS keys (166 bits), PubChem fingerprints (881 bits), and E-States fingerprints (79 bits). These were integrated to produce reaction fingerprints. The data were sourced from SABIO RK, providing a concrete framework to support training procedures. After the data preprocessing stage, the dataset was randomly split into a training set (70%), a validation set (10%), and a test set (20%), ensuring unique amino acid sequences for each subset. The data points with structures similar to those used to train the model as well as uncommon reactions were employed to test the model further. The developed models were optimized during training to predict Vmax values efficiently and reliably. By utilizing a fully connected neural network, these models can be applied to all organisms. The amino acid proportions of enzymes were also tested, which revealed that the amino acid content was an unreliable predictor of the Vmax. During testing, the model demonstrated better performance on known structures than on unseen data. In the given use case, the model trained solely on enzyme representations achieved an R-squared of 0.45 on unseen data and 0.70 on known structures. When enzyme representations were integrated with RCDK fingerprints, the model achieved an R-squared of 0.46 for unseen data and 0.62 for known structures.

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AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology

Abstract: Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that...

Cited by 3 publications

References 75 publications

All-Atom Biomolecular Simulation in the Exascale Era

All-Atom Biomolecular Simulation in the Exascale Era

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)

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