What we do

Research

Using computational tools to uncover the rules of biology and enable new therapeutic discoveries.

Our Approach

Three research pillars

01

Developing computational methods

We develop new, cutting-edge computational methods to predict and analyze the interactions of small organic molecules (either synthetic or from natural sources) with biological macromolecular structures such as proteins and nucleic acids.

02

Understanding biological methanisms

By combining these tools with other molecular modeling techniques, we investigate biological processes from mechanistic and thermodynamic perspectives. This work helps us unravel the physico-chemical principles that govern complex biological events.

03

Applications to drug discovery

We apply our methods to rapidly explore chemical space and identify molecular modulators of therapeutically relevant targets. These efforts are carried out in collaboration with researchers at Scripps Research, as well as partners in academia and pharmaceutical industry.

Research area 01

AutoDock suite

We develop computational tools for structure-based drug design, with a focus on molecular docking. AutoDock and AutoDock Vina, originally created in the Molecular Graphics Lab (now CCSB), are among the most widely used open-source docking programs. Building on this foundation, we are developing next-generation docking engines and virtual screening tools with improved energy potentials, more accurate binding free energy estimates, and innovative protocols for drug design.

Our goal is to make docking faster, more accurate, and more chemically meaningful. To achieve this, we are improving the description of chemical, physical, and thermodynamic terms, including torsional preferences of rotatable bonds, non-covalent interactions, and solvation effects, drawing on modern force fields, electronic structure methods, and molecular dynamics simulations. In parallel, we explore new search algorithms and leverage GPU acceleration to navigate the energy landscape efficiently and to scale virtual screening.

Key publications
  • Charting hydrogen bond anisotropy (JCTC 2020)
  • Computational protein–ligand docking and virtual drug screening with the AutoDock suite (Nat. Protoc. 2016)
  • A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking (J. Med. Chem. 2012)
  • AutoDock4Zn: an improved AutoDock force field for small-molecule docking to zinc metalloproteins (J. Chem. Inf. Mod. 2014)
  • AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions (Bioinformatics 2019)
  • Accelerating AutoDock4 with GPUs and gradient-based local search (J. Chem. Theory Comput. 2019)
  • D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU (J. Comput. Aided Mol. Des. 2019)
Industry partners
Research area 02

Covalent inhibitor high-throughput screening

Key publications
  • Proteome-wide covalent ligand discovery in native biological systems (Nature 2016)
  • “Inverse drug discovery” strategy to identify proteins targeted by latent electrophiles as exemplified by aryl fluorosulfates (JACS 2018)
  • Covalent docking using AutoDock: two-point attractor and flexible side chain methods (Protein Sci. 2018)
  • SuFEx-enabled, agnostic discovery of covalent inhibitors of human neutrophil elastase (PNAS 2019)
  • Global profiling of lysine reactivity and ligandability in the human proteome (Nat. Chem. 2017)
  • Integrative X-ray structure and molecular modeling for the rationalization of procaspase-8 inhibitor potency and selectivity (ACS Chem. Biol. 2020)
  • Expedited mapping of the ligandable proteome using fully functionalized enantiomeric probe pairs (Nat. Chem. 2019)

Modeling of covalent binders is non-trivial, especially in high-throughput settings. While several covalent docking methods exist, most are effective only when the target structure and covalent binding site are already known. To overcome this barrier, we are developing Reactive Docking, a method that predicts covalent binding sites by simulating the reaction between a protein residue and a ligand warhead. This approach supports diverse residues and warhead chemistries. It has been successfully applied to multiple targets and is suitable for HTVS to discover novel covalent binding sites across different proteins.

Research area 03

HIV-1 life cycle

The search for drugs that target different stages of the HIV-1 life cycle remains a subject of great interest, since current therapies cannot fully eradicate the infection. We are focusing on the HIV-1 capsid protein, using a strategy that combines conventional and reactive docking for the virtual screening of small molecules that could modulate the viral assembly. In particular, our Reactive Docking approach is applied to identify small molecules with SuFEx warheads that could tightly bind to the identified pocket.

Key publications
  • Structural basis for strand-transfer inhibitor binding to HIV intasomes (Science 2020)
  • Integrative modeling of the HIV-1 ribonucleoprotein complex (PLoS Comput. Biol. 2019)
  • A new class of allosteric HIV-1 integrase inhibitors identified by crystallographic fragment screening of the catalytic core domain (JBC 2016)
  • Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease (J. Phys. Chem. B 2015)
  • Blind prediction of HIV integrase binding from the SAMPL4 challenge (J. Comput. Aided Mol. Des. 2014)
  • Crystallographic fragment-based drug discovery: use of a brominated fragment library targeting HIV protease (Chem. Biol. Drug Des. 2014)
  • 3D molecular models of whole HIV-1 virions generated with cellPACK (Faraday Discuss. 2014)
  • Novel intersubunit interaction critical for HIV-1 core assembly defines a potentially targetable inhibitor binding pocket (mBio 2019)
Initiative
Research area 04

Molecular modeling applications

Key publications
  • Structural basis of altered potency and efficacy displayed by a major in vivo metabolite of the antidiabetic PPARγ drug pioglitazone (J. Med. Chem. 2019)
  • Humanized GPIbα–von Willebrand factor interaction in the mouse (Blood Adv. 2018)
  • Natural product anacardic acid from cashew nut shells stimulates neutrophil extracellular trap production and bactericidal activity (JBC 2016)
  • Directional phosphorylation and nuclear transport of the splicing factor SRSF1 is regulated by an RNA recognition motif (JBC 2016)

We use molecular modeling techniques such as molecular dynamics, pharmacophore modeling, and virtual screenings to study protein-ligand and protein-protein interactions. These approaches help reveal the structural basis of drug action, immune regulation, RNA processing, and other biological processes, guiding therapeutic discovery and design.