Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Overlapping representations of food and social stimuli in mouse VTA dopamine neurons.

Dopamine neurons of the ventral tegmental area (VTADA) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear whether the same or different VTADA neurons encode these different stimuli. To address this question, we performed two-photon calcium imaging in mice presented with food and conspecifics and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that increasing motivation for one stimulus increases overlap. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone-related genes in individual VTADA neurons. Taken together, our functional and transcriptional data suggest overlapping VTADA populations underlie food and social motivation.

Identification of GDC-1971 (RLY-1971), a SHP2 Inhibitor Designed for the Treatment of Solid Tumors.

Protein tyrosine phosphatase SHP2 mediates RAS-driven MAPK signaling and has emerged in recent years as a target of interest in oncology, both for treating with a single agent and in combination with a KRAS inhibitor. We were drawn to the pharmacological potential of SHP2 inhibition, especially following the initial observation that drug-like compounds could bind an allosteric site and enforce a closed, inactive state of the enzyme. Here, we describe the identification and characterization of GDC-1971 (formerly RLY-1971), a SHP2 inhibitor currently in clinical trials in combination with KRAS G12C inhibitor divarasib (GDC-6036) for the treatment of solid tumors driven by a KRAS G12C mutation.

A Conserved Local Structural Motif Controls the Kinetics of PTP1B Catalysis.

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin signaling pathways, making it a highly attractive target for the treatment of type II diabetes. For PTP1B to perform its enzymatic function, a loop referred to as the "WPD loop" must transition between open (catalytically incompetent) and closed (catalytically competent) conformations, which have both been resolved by X-ray crystallography. Although prior studies have established this transition as the rate-limiting step for catalysis, the transition mechanism for PTP1B and other PTPs has been unclear. Here we present an atomically detailed model of WPD loop transitions in PTP1B based on unbiased, long-timescale molecular dynamics simulations and weighted ensemble simulations. We found that a specific WPD loop region─the PDFG motif─acted as the key conformational switch, with structural changes to the motif being necessary and sufficient for transitions between long-lived open and closed states of the loop. Simulations starting from the closed state repeatedly visited open states of the loop that quickly closed again unless the infrequent conformational switching of the motif stabilized the open state. The functional importance of the PDFG motif is supported by the fact that it is well conserved across PTPs. Bioinformatic analysis shows that the PDFG motif is also conserved, and adopts two distinct conformations, in deiminases, and the related DFG motif is known to function as a conformational switch in many kinases, suggesting that PDFG-like motifs may control transitions between structurally distinct, long-lived conformational states in multiple protein families.

Overlapping representations of food and social stimuli in VTA dopamine neurons.

Dopamine neurons of the ventral tegmental area (VTA DA ) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear if the same or different VTA DA neurons encode these different stimuli. To address this question, we performed 2-photon calcium imaging in mice presented with food and conspecifics, and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that modifying motivation for one stimulus affects responses to both stimuli. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone related genes in individual VTA DA neurons. Taken together, our functional and transcriptional data suggest overlapping VTA DA populations underlie food and social motivation.

Discovery and Validation of the Binding Poses of Allosteric Fragment Hits to Protein Tyrosine Phosphatase 1b: From Molecular Dynamics Simulations to X-ray Crystallography.

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.

Behavioural and dopaminergic signatures of resilience.

Chronic stress can have lasting adverse consequences in some individuals, yet others are resilient to the same stressor1,2. Susceptible and resilient individuals exhibit differences in the intrinsic properties of mesolimbic dopamine (DA) neurons after the stressful experience is over3-8. However, the causal links between DA, behaviour during stress and individual differences in resilience are unknown. Here we recorded behaviour in mice simultaneously with DA neuron activity in projections to the nucleus accumbens (NAc) (which signals reward9-12) and the tail striatum (TS) (which signals threat13-16) during social defeat. Supervised and unsupervised behavioural quantification revealed that during stress, resilient and susceptible mice use different behavioural strategies and have distinct activity patterns in DA terminals in the NAc (but not the TS). Neurally, resilient mice have greater activity near the aggressor, including at the onset of fighting back. Conversely, susceptible mice have greater activity at the offset of attacks and onset of fleeing. We also performed optogenetic stimulation of NAc-projecting DA neurons in open loop (randomly timed) during defeat or timed to specific behaviours using real-time behavioural classification. Both open-loop and fighting-back-timed activation promoted resilience and reorganized behaviour during defeat towards resilience-associated patterns. Together, these data provide a link between DA neural activity, resilience and resilience-associated behaviour during the experience of stress.

Fragment Hits: What do They Look Like and How do They Bind?

A "fragment hit", a molecule of low molecular weight that has been validated to bind to a target protein, can be an effective chemical starting point for a drug discovery project. Our ability to find and progress fragment hits could potentially be improved by enhancing our understanding of their binding properties, which to date has largely been based on tacit knowledge and reports from individual projects. In the work reported here, we systematically analyzed the molecular and binding properties of fragment hits using 489 published protein-fragment complexes. We identified a number of notable features that these hits tend to have in common, including preferences in buried surface area upon binding, hydrogen bonding and other directional interactions with the protein targets, structural topology, functional-group occurrence, and degree of carbon saturation. In the future, taking account of these preferences in designing and selecting fragments to screen against protein targets may increase the chances of success in fragment screening campaigns.

Fast animal pose estimation using deep neural networks.

The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positions of animal body parts. This framework consists of a graphical interface for labeling of body parts and training the network. LEAP offers fast prediction on new data, and training with as few as 100 frames results in 95% of peak performance. We validated LEAP using videos of freely behaving fruit flies and tracked 32 distinct points to describe the pose of the head, body, wings and legs, with an error rate of <3% of body length. We recapitulated reported findings on insect gait dynamics and demonstrated LEAP's applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice.

DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells.

Mapping of protein-protein interactions (PPIs) is critical for understanding protein function and complex biological processes. Here, we present DULIP, a dual luminescence-based co-immunoprecipitation assay, for systematic PPI mapping in mammalian cells. DULIP is a second-generation luminescence-based PPI screening method for the systematic and quantitative analysis of co-immunoprecipitations using two different luciferase tags. Benchmarking studies with positive and negative PPI reference sets revealed that DULIP allows the detection of interactions with high sensitivity and specificity. Furthermore, the analysis of a PPI reference set with known binding affinities demonstrated that both low- and high-affinity interactions can be detected with DULIP assays. Finally, using the well-characterized interaction between Syntaxin-1 and Munc18, we found that DULIP is capable of detecting the effects of point mutations on interaction strength. Taken together, our studies demonstrate that DULIP is a sensitive and reliable method of great utility for systematic interactome research. It can be applied for interaction screening and validation of PPIs in mammalian cells. Moreover, DULIP permits the specific analysis of mutation-dependent binding patterns.