Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2\'s strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

N-methyl-D-aspartate (NMDA) receptors are heterotetrametric ion channels composed of two obligatory GluN1 subunits and two alternative GluN2 or GluN3 subunits, forming GluN1-N2, GluN1-N3, and GluN1-N2-N3 type of NMDA receptors. Extensive research has focused on the functional and structural properties of conventional GluN1-GluN2 NMDA receptors due to their early discovery and high expression levels. However, the knowledge of unconventional GluN1-N3 NMDA receptors remains limited. In this study, we modeled the GluN1-N3A, GluN1-N3B, and GluN1-N3A-N3B NMDA receptors using deep-learned protein-language predication algorithms AlphaFold and RoseTTAFold All-Atom. We then compared these structures with GluN1-N2 and GluN1-N3A receptor cryo-EM structures and found that GluN1-N3 receptors have distinct properties in subunit arrangement, domain swap, and domain interaction. Furthermore, we predicted the agonist- or antagonist-bound structures, highlighting the key molecular-residue interactions. Our findings shed new light on the structural and functional diversity of NMDA receptors and provide a new direction for drug development. This study uses advanced AI algorithms to model GluN1-N3 NMDA receptors, revealing unique structural properties and interactions compared to conventional GluN1-N2 receptors. By highlighting key molecular-residue interactions and predicting ligand-bound structures, our research enhances the understanding of NMDA receptor diversity and offers new insights for targeted drug development.

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme\'s mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

Cellular trafficking between organelles is typically assured by short motifs that contact carrier proteins to transport them to their destination. Ubiquitin E3 ligase RING finger protein 13 (RNF13), a regulator of proliferation, apoptosis, and protein trafficking, localizes to endolysosomal compartments through the binding of a dileucine motif to clathrin adaptor protein complex AP-3. Mutations within this motif reduce the ability of RNF13 to interact with AP-3. Here, our study shows the discovery of a glutamine-based motif that resembles a tyrosine-based motif within RNF13\'s C-terminal region that binds to the clathrin adaptor protein complex AP-1, notably without a functional interaction with AP-3. Using biochemical, molecular, and cellular approaches in HeLa cells, our study demonstrates that a RNF13 dileucine variant uses an AP-1-dependent pathway to be exported from the Golgi towards the endosomal compartment. Overall, this study provides mechanistic insights into the alternate route used by variant of RNF13\'s dileucine sorting motif.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

BACKGROUND: The PICK1 PDZ domain has been identified as a potential drug target for neurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5, have been discovered experimentally to bind to the PDZ domain with a relatively high binding affinity. With the rapid growth of computational research, there is an urgent need for more efficient computational methods to design viable ligands that target proteins.
METHODS: Recently, a newly developed program called AfDesign (part of ColabDesign) at https:// github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has been suggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitating the ligand development process. To evaluate the performance of this program, we explored its ability to target the PICK1 PDZ domain, given our current understanding of it. We found that the designated length of the ligand and the number of recycles play vital roles in generating ligands with optimal properties.
RESULTS: Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparable ligands to that of prior identified ligands. Moreover, these designed ligands displayed significantly lower binding energy compared to manually created sequences.
CONCLUSIONS: This work demonstrated that AfDesign can potentially be a powerful tool to facilitate the exploration of the ligand space for the purpose of targeting PDZ domains.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.

This study presents a structural phylogenetic analysis of plant defensive peptides, revealing their evolutionary relationships, structural diversification, and functional adaptations. Utilizing a robust dataset comprising both experimental and predicted structures sourced from the RCSB Protein Data Bank and AlphaFold DB, we constructed a detailed phylogenetic tree to elucidate the distinct evolutionary paths of plant defensive peptide families. Our findings showcase the evolutionary intricacies of defensive peptides, highlighting their diversity and the conservation of key structural motifs critical to their antimicrobial or defensive functions. The results also underscore the adaptive significance of defensive peptides in plant evolution, highlighting their roles in responding to ecological pressures and pathogen interactions.

The human Atg8 family member GABARAP is involved in numerous autophagy-related and -unrelated processes. We recently observed that specifically the deficiency of GABARAP enhances epidermal growth factor receptor (EGFR) degradation upon ligand stimulation. Here, we report on two putative LC3-interacting regions (LIRs) within EGFR, the first of which (LIR1) is selected as a GABARAP binding site in silico. Indeed, in vitro interaction studies reveal preferential binding of LIR1 to GABARAP and GABARAPL1. Our X-ray data demonstrate interaction of core LIR1 residues FLPV with both hydrophobic pockets of GABARAP suggesting canonical binding. Although LIR1 occupies the LIR docking site, GABARAP Y49 and L50 appear dispensable in this case. Our data support the hypothesis that GABARAP affects the fate of EGFR at least in part through direct binding.

Crystallography at low resolution must determine the atomic model from less experimental observations, which is challenging in the absence of a model. In addition, model bias is more severe when independent experimental data are scarce. Our methods solve the phase problem by combining the location of accurate model fragments using Phaser with density modification and interpretation of the resulting maps using SHELXE. From a partial, correct structure, the density modification process and the stereochemical constraints draw the rest of the structure, validating the result. This same principle is now exploited at low resolution. Coiled coils are important, ubiquitous structures but notoriously difficult to phase and to predict. Both correct solutions and incorrect ones are poorly discriminated by the crystallographic figures of merit as long as helices are correctly oriented. We incorporate coiled-coil verification, designed to set up competing, incompatible structural hypotheses to probe both the results and establish the power of the data to discriminate them. Efficiency of coiled-coil phasing and validation in test cases from 3 to 4 Å is demonstrated in ARCIMBOLDO_LITE, placing single helices, and in ARCIMBOLDO_SHREDDER, with fragments derived from AlphaFold models. SHELXE tracing at low resolution has been enhanced, maintaining its local character but extending the environment assessment. For non-helical structures, verification is demonstrated in the fragment location process. Its use is exemplified with the solution of the VSR1 structure at 3.5 Å, depending on LLG optimization and the emergence of new features in the electron density. Relying on verification, we have extended the use of the ARCIMBOLDO software to low resolution.