AlphaFold, Artificial Intelligence (AI), and Allostery

Abstract

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Figure 1

Current strategy of allosteric drug discovery in computational structural biology employing the AlphaFold program with artificial intelligence (AI)-powered methods (top panel). Experimental instruments, such as X-ray crystallography, cryo-electron microscopy (cryo-EM), and nuclear magnetic resonance (NMR) can resolve protein structures, but often miss the coordinates of highly fluctuating regions in the protein structure. AlphaFold can predict the missing coordinates of these regions. The resulting structure can be subjected to molecular dynamics (MD) simulations that provide conformational dynamics, conformational changes, and folding characteristics of the protein. An example is shown for Src homology region 2-containing protein tyrosine phosphatase 2 (SHP2) (bottom panel). The X-ray structure of SHP2 (PDB ID: 4DGP) misses residues in two flexible regions, which can be predicted by AlphaFold. SHP2 contains two Src homology 2 (SH2) domains (nSH2 and cSH2) and a protein tyrosine phosphatase (PTP) domain.

Figure 2

Structural ensembles for B-Raf activation. The snapshots for B-Raf kinase domains (top panels) are generated from the protein databank (PDB). The representative inactive OFF-state conformation (PDB ID: 3SKC) and active ON-state conformation (PDB ID: 6UAN) are highlighted in blue and red, respectively. The free energy landscape of B-Raf kinase domain depicting the population shift from OFF-state to ON-state upon activation (middle panel). Highlighted activation segments of αC-helix and A-loop representing the side by side comparisons between the single structure predicted by AlphaFold and the representative B-Raf conformations of both inactive OFF-state and active ON-state (bottom panels). The AlphaFold structure falls into neither the active ON-state nor the inactive OFF-state.

Figure 3

Human–microbiome PPIs promote GTPase activation. Human cell division control protein 42 homologue (Cdc42) is a small GTPase of the Rho family, involved in cell cycle. In human cells, it is activated by guanine-nucleotide exchange factors (GEFs), such as DOCK9 (PDB ID: 2WMO), by transforming the inactive GDP-bound to the active GTP-bound forms. Bacterial secretes toxins or effectors mimicking the GEF proteins, such as SopE (PDB: 1GZS) from Salmonella and MAP (PDB: 3GCG) from Escherichia coli, can interact with Cdc42 and activate it. The interaction surfaces of these bacterial GEF mimicries resemble the host protein, allowing them to mimic and compete with the host protein interactions. PPIs, protein–protein interactions; HMIs, host–(or human−) microbiome interactions. Ongoing work incorporates AI into the HMI prediction algorithm. If the structures of the human or microbe are unable, AlphaFold can generate them.