Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

The historically dominant SARS-CoV-2 Delta variant and the currently dominant Omicron variants carry a T492I substitution within the non-structural protein 4 (NSP4). Based on in silico analyses, we hypothesized that the T492I mutation increases viral transmissibility and adaptability, which we confirmed with competition experiments in hamster and human airway tissue culture models. Furthermore, we showed that the T492I mutation increases the replication capacity and infectiveness of the virus and improves its ability to evade host immune responses. Mechanistically, the T492I mutation increases the cleavage efficiency of the viral main protease NSP5 by enhancing enzyme-substrate binding, which increases production of nearly all non-structural proteins processed by NSP5. Importantly, the T492I mutation suppresses viral-RNA-associated chemokine production in monocytic macrophages, which may contribute to the attenuated pathogenicity of Omicron variants. Our results highlight the importance of NSP4 adaptation in the evolutionary dynamics of SARS-CoV-2.

Interferon-gamma (IFN-γ) signaling is necessary for the proinflammatory activation of macrophages but IFN-γ-independent pathways, for which the initiating stimuli and downstream mechanisms are lesser known, also contribute. Here we identify, by high-content screening, SEPTIN2 (SEPT2) as a negative regulation of IFN-γ-independent macrophage autoactivation. Mechanistically, endoplasmic reticulum (ER) stress induces the expression of SEPT2, which balances the competition between acetylation and ubiquitination of heat shock protein 5 at position Lysine 327, thereby alleviating ER stress and constraining M1-like polarization and proinflammatory cytokine release. Disruption of this negative feedback regulation leads to the accumulation of unfolded proteins, resulting in accelerated M1-like polarization, excessive inflammation and tissue damage. Our study thus uncovers an IFN-γ-independent macrophage proinflammatory autoactivation pathway and suggests that SEPT2 may play a role in the prevention or resolution of inflammation during infection.

Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo and IOholo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Moreover, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.

ABSTRACTThe historically dominant SARS-CoV-2 Delta variants and the currently dominant Omicron variants carry a T492I substitution within the non-structural protein 4 (NSP4). Based on a combination ofin silicoanalyses, we predicted that the T492I mutation increases the transmissibility and adaptability of the virus. We confirmed this hypothesis by performing competition experiments in hamsters and in human airway tissue culture models. Furthermore, we show that the T492I mutation also increases the replication capacity and infectiveness of the virus, and improves its ability to evade antibody neutralization induced by previous variants. Mechanistically, the T492I mutation increases cleavage efficiency of the viral main protease NSP5 by enhancing enzyme-substrate binding, resulting in increased production of nearly all non-structural proteins processed by NSP5. Importantly, T492I mutation suppresses the viral RNA associated chemokines in monocytic macrophages, which may contribute to the attenuated pathogenicity of Omicron variants. Our results highlight the importance of the NSP4 mutation in the evolutionary dynamics of SARS-CoV-2 and identify a novel target for the development of broad-spectrum antiviral agents.

The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named 'Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)' was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.