Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Theoretical evaluation of the biological activity of hydrogen

Hydrogen (H2), the simplest and most ubiquitous molecule in the universe, has garnered significant scientific interest over the past two decades because of its potential as an effective antioxidant and anti-inflammatory agent. Traditionally considered inert, H2 is now being re-evaluated for its unique bioactive properties. H2 selectively neutralizes reactive oxygen and nitrogen species, mitigating oxidative stress without disrupting essential cellular functions. This review therefore aims to provide a theoretical evaluation of the biological activity of H2, focusing on its pharmacokinetics, including absorption, distribution, and retention within biological systems. The pharmacokinetic profile of H2 is crucial for understanding its potential therapeutic applications. The interaction of H2 with protein pockets is of particular interest, as these sites may serve as reservoirs or active sites for H2, influencing its biological activity and retention time. Additionally, the impact of H2 on cellular signaling pathways, including those regulating glucose metabolism and oxidative stress responses, will be explored, offering insights into its potential as a modulator of metabolic and redox homeostasis. Finally, interactions with ferromagnetic molecules within biological environments, as well as effects on cellular signaling mechanisms, add another layer of complexity to the biological role of H2. By synthesizing the current research, this review seeks to elucidate the underlying mechanisms by which H2 may exert therapeutic effects while also identifying critical areas for further investigation. Understanding these aspects is essential for fully characterizing the pharmacodynamic profile of H2 and assessing its clinical potential in the treatment of oxidative stress-related disorders.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein's conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy.

Assessing interaction recovery of predicted protein-ligand poses

The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.Scientific Contribution The interaction analysis used in this study is provided as a python package at https://github.com/Exscientia/plif_validity .

Unifying sequence-structure coding for advanced protein engineering via a multimodal diffusion transformer

Modern protein engineering demands integrated sequence-structure representations to tackle key challenges in designing, modifying, and evolving proteins for specific functions. While sequence-based methods are promising for generating novel proteins, incorporating structure-oriented information improves the success rate and helps target corresponding functions. Therefore, rather than relying solely on sequence or structure-based approaches, a consensus strategy is essential. Here, we introduce ProTokens, machine-learned "amino acids" derived from structural databases via self-supervised learning, providing a compact yet information-rich representation that bridges sequence and structure modalities. Instead of treating sequences and structures separately, we build PT-DiT, a multimodal diffusion transformer-based model that integrates both into a unified representation, enabling protein engineering in a joint sequence-structure space, streamlining the design process and facilitating the efficient encoding of 3D folds, contextual protein design, sampling of metastable states, and directed evolution for diverse objectives. Therefore, as a unified solution for in silico protein engineering, PT-DiT leverages sequence and structure insights to realize functional protein design.

CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction

Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challenging because they are often discovered serendipitously when both ligand-binding (holo) and ligand-free (apo) states are experimentally determined. Here, we introduce CrypTothML, a novel framework that integrates mixed-solvent molecular dynamics (MSMD) simulations and machine learning to predict cryptic sites accurately. CrypTothML first identifies hotspots through MSMD simulations using six chemically diverse probes (benzene, dimethyl-ether, phenol, methyl-imidazole, acetonitrile, and ethylene glycol). A machine learning model then ranks these hotspots based on their likelihood of being cryptic sites, incorporating both hotspot-derived and protein-specific features. Evaluation on a curated dataset demonstrated that CrypTothML outperforms recent machine learning-based methods, achieving an AUC-ROC of 0.88 and successfully identifying cryptic sites missed by other methods. Additionally, CrypTothML ranked cryptic sites as the top prediction more frequently than existing methods. This approach provides a powerful strategy for accelerating drug discovery and designing allosteric drugs.

Profiling of Protein-Coding Missense Mutations in Mendelian Rare Diseases: Clues from Structural Bioinformatics

The growing availability of protein structural data from experimental methods and accurate predictive models provides the opportunity to investigate the molecular origins of rare diseases (RDs) reviewed in the Orpha.net database. In this study, we analyzed the topology of 5728 missense mutation sites involved in Mendelian RDs (MRDs), forming the basis of our structural bioinformatics investigation. Each mutation site was characterized by side-chain position within the overall 3D protein structure and side-chain orientation. Atom depth quantitation, achieved by using SADIC v2.0, allowed the classification of all the mutation sites listed in our database. Particular attention was given to mutations where smaller amino acids replaced bulky, outward-oriented residues in the outer structural layers. Our findings reveal that structural features that could lead to the formation of void spaces in the outer protein region are very frequent. Notably, we identified 722 cases where MRD-associated mutations could generate new surface pockets with the potential to accommodate pharmaceutical ligands. Molecular dynamics (MD) simulations further supported the prevalence of cryptic pocket formation in a subset of drug-binding protein candidates, underscoring their potential for structure-based drug discovery in RDs.

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthosteric counterparts; multiple binding sites of interest are considered, and often allosteric binding is only observed in particular protein conformations. Blind docking methods show potential in virtual screening allosteric ligands, and deep learning methods, such as DiffDock, achieve state-of-the-art performance on protein-ligand complex prediction benchmarks compared to traditional docking methods such as Vina and Lin_F9. To this aim, we explore the utility of a data-driven platform called the minimum distance matrix representation (MDMR) to retrospectively predict recently discovered allosteric inhibitors complexed with Cyclin-Dependent Kinase (CDK) 2. In contrast to other protein complex representations, it uses the minimum residue-residue (or residue-ligand) distance as a feature that prioritizes the formation of interactions. Analysis of this representation highlights the variety of protein conformations and ligand binding modes, and we identify an intermediate protein conformation that other heuristic-based kinase conformation classification methods do not distinguish. Next, we design self- and cross-docking benchmarks to assess whether docking methods can predict both orthosteric and allosteric binding modes and if prospective success is conditional on the selection of the protein receptor conformation, respectively. We find that a combined method, DiffDock followed by Lin_F9 Local Re-Docking (DiffDock + LRD), can predict both orthosteric and allosteric binding modes, and the intermediate conformation must be selected to predict the allosteric pose. In summary, this work highlights the value of a data-driven method to explore protein conformations and ligand binding modes and outlines the challenges of SBDD of allosteric compounds.

Discovering cryptic pocket opening and binding of a stimulant derivative in a vestibular site of the 5-HT3A receptor

A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. Here, we used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor. We first applied a molecular dynamics simulation-based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.

Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery

Allosteric regulation is a fundamental mechanism in enzyme function, enabling dynamic modulation of activity through ligand binding at sites distal to the active site. Allosteric modulators have gained significant attention due to their unique advantages, including enhanced specificity, reduced off-target effects, and the potential for synergistic interaction with orthosteric agents. However, the inherent complexity of allosteric mechanisms has posed challenges to the systematic discovery and design of allosteric modulators. This review discusses recent advancements in computational methodologies for identifying and characterizing allosteric sites in enzymes, emphasizing techniques such as molecular dynamics (MD) simulations, enhanced sampling methods, normal mode analysis (NMA), evolutionary conservation analysis, and machine learning (ML) approaches. Advanced tools like PASSer, AlloReverse, and AlphaFold have further enhanced the understanding of allosteric mechanisms and facilitated the design of selective allosteric modulators. Case studies on enzymes such as Sirtuin 6 (SIRT6) and MAPK/ERK kinase (MEK) demonstrate the practical applications of these approaches in drug discovery. By integrating computational predictions with experimental validation, this review highlights the transformative potential of computational strategies in advancing allosteric drug discovery, offering innovative opportunities to regulate enzyme activity for therapeutic benefits.

Enhanced identification of small molecules binding to hnRNPA1 via cryptic pockets mapping coupled with X-ray fragment screening

The human heterogeneous nuclear ribonucleoprotein (hnRNP) A1 is a prototypical RNA-binding protein essential for regulating a wide range of post-transcriptional events in cells. As a multifunctional protein with a key role in RNA metabolism, deregulation of its functions has been linked to neurodegenerative diseases, tumor aggressiveness, and chemoresistance, which has fuelled efforts to develop novel therapeutics that modulate its RNA-binding activities. Here, using a combination of molecular dynamics simulations and graph neural network pocket predictions, we showed that hnRNPA1 N-terminal RNA-binding domain (unwinding protein 1 [UP1]) contains several cryptic pockets capable of binding small molecules. To identify chemical entities for the development of potent drug candidates and experimentally validate identified druggable hotspots, we carried out a large fragment screening on UP1 protein crystals. Our screen identified 36 hits that extensively sample UP1 functional regions involved in RNA recognition and binding as well as map hotspots onto novel protein interaction surfaces. We observed a wide range of ligand-induced conformational variation by stabilization of dynamic protein regions. Our high-resolution structures, the first of an hnRNP in complex with a fragment or small molecule, provide rapid routes for the rational development of a range of different inhibitors and chemical tools for studying molecular mechanisms of hnRNPA1-mediated splicing regulation.

Comparative structural insights and functional analysis for the distinct unbound states of Human AGO proteins

The four human Argonaute (AGO) proteins, critical in RNA interference and gene regulation, exhibit high sequence and structural similarity but differ functionally. We investigated the underexplored structural relationships of these paralogs through microsecond-scale molecular dynamics simulations. Our findings reveal that AGO proteins adopt similar, yet unsynchronized, open-close states. We observed similar and unique local conformations, interdomain distances and intramolecular interactions. Conformational differences at GW182/ZSWIM8 interaction sites and in catalytic/pseudo-catalytic tetrads were minimal. Tetrads display conserved movements, interacting with distant miRNA binding residues. We pinpointed long common protein subsequences with consistent molecular movement but varying solvent accessibility per AGO. We observed diverse conformational patterns at the post-transcriptional sites of the AGOs, except for AGO4. By combining simulation data with large datasets of experimental structures and AlphaFold's predictions, we identified proteins with genomic and proteomic similarities. Some of the identified proteins operate in the mitosis pathway, sharing mitosis-related interactors and miRNA targets. Additionally, we suggest that AGOs interact with a mitosis initiator, zinc ion, by predicting potential binding sites and detecting structurally similar proteins with the same function. These findings further advance our understanding for the human AGO protein family and their role in central cellular processes.

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens

The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target for antibiotic development due to its essential role in transcription. RNAP is a molecular motor, and its function relies heavily on the dynamic shifts between multiple conformational states. While biochemical and structural experimental methods offer crucial insights into static RNAP-drug interactions, they fall short in capturing the dynamics at a molecular level. By integrating experimental data with advanced computational techniques like Markov State Models (MSMs), Generalized Master Equation (GME) Models and other machine-learning models constructed from molecular dynamics (MD) simulations, researchers can elucidate novel cryptic pockets that open transiently for antibiotic compounds and gain a more nuanced and comprehensive understanding of RNAP-drug interactions. This integrated approach not only deepens our fundamental knowledge but also enables more targeted and efficient antibiotic design strategies. In this Perspective, we highlight how this synergy between experimental and computational methods has the potential to open new pathways for innovative drug design and combination therapies that may help turn the tide in the ongoing battle against antibiotic-resistant bacteria.

Revisiting the Plasmodium falciparum druggable genome using predicted structures and data mining

Identification of novel drug targets is a key component of modern drug discovery. While antimalarial targets are often identified through the mechanism of action studies on phenotypically derived inhibitors, this method tends to be time- and resource-consuming. The discoverable target space is also constrained by existing compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction, we systematically assessed the Plasmodium falciparum genome and identified 867 candidate protein targets with evidence of small-molecule binding and blood-stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 27 high-priority antimalarial target candidates. This study also provides a genome-wide data resource for P. falciparum and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

Classifying covalent protein binders by their targeted binding site

Covalent protein targeting represents a powerful tool for protein characterization, identification, and activity modulation. The safety of covalent therapeutics was questioned for many years due to the possibility of off-target binding and subsequent potential toxicity. Researchers have recently, however, demonstrated many covalent binders as safe, potent, and long-acting therapeutics. Moreover, they have achieved selective targeting among proteins with high structural similarities, overcome mutation-induced resistance, and obtained higher potency compared to non-covalent binders. In this review, we highlight the different classes of binding sites on a target protein that could be addressed by a covalent binder. Upon folding, proteins generate various concavities available for covalent modifications. Selective targeting to a specific site is driven by differences in the geometry and physicochemical properties of the binding pocket residues as well as the geometry and reactivity of the covalent modifier "warhead". According to the warhead reactivity and the nature of the binding region, covalent binders can alter or lock a targeted protein conformation and inhibit or enhance its activity. We survey these various modification sites using case studies of recently discovered covalent binders, bringing to the fore the versatile application of covalent protein binders with respect to drug discovery approaches.

Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds?

Covalent-allosteric inhibitors (CAIs) may achieve the best of both worlds: increased potency, long-lasting effects, and reduced drug resistance typical of covalent ligands, along with enhanced specificity and decreased toxicity inherent in allosteric modulators. Therefore, CAIs can be an effective strategy to transform many undruggable targets into druggable ones. However, CAIs are challenging to design. In this perspective, we analyze the discovery of known CAIs targeting three protein families: protein phosphatases, protein kinases, and GTPases. We also discuss how computational methods and tools can play a role in addressing the practical challenges of rational CAI design.

Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Charting future innovations is challenging. Yet, allosteric and orthosteric anticancer drugs are undergoing a revolution and taxing unresolved dilemmas await. Among the imaginative innovations, here we discuss cereblon and thalidomide derivatives as a means of recruiting neosubstrates and their degradation, allosteric heterogeneous bifunctional drugs like PROTACs, drugging phosphatases, inducers of targeted posttranslational protein modifications, antibody-drug conjugates, exploiting membrane interactions to increase local concentration, stabilizing the folded state, and more. These couple with harnessing allosteric cryptic pockets whose discovery offers more options to modulate the affinity of orthosteric, active site inhibitors. Added to these are strategies to counter drug resistance through drug combinations co-targeting pathways to bypass signaling blockades. Here, we discuss on the molecular and cellular levels, such inspiring advances, provide examples of their applications, their mechanisms and rational. We start with an overview on difficult to target proteins and their properties-rarely, if ever-conceptualized before, discuss emerging innovative drugs, and proceed to the increasingly popular allosteric cryptic pockets-their advantages-and critically, issues to be aware of. We follow with drug resistance and in-depth discussion of tumor heterogeneity. Heterogeneity is a hallmark of highly aggressive cancers, the core of drug resistance unresolved challenge. We discuss potential ways to target heterogeneity by predicting it. The increase in experimental and clinical data, computed (cell-type specific) interactomes, capturing transient cryptic pockets, learned drug resistance, workings of regulatory mechanisms, heterogeneity, and resistance-based cell signaling drug combinations, assisted by AI-driven reasoning and recognition, couple with creative allosteric drug discovery, charting future innovations.

Computational advances in discovering cryptic pockets for drug discovery

A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.

Targeting cryptic allosteric sites of G protein-coupled receptors as a novel strategy for biased drug discovery

G protein-coupled receptors (GPCRs) represent the largest family of membrane receptors and are highly effective targets for therapeutic drugs. GPCRs couple different downstream effectors, including G proteins (such as Gi/o, Gs, G12, and Gq) and β-arrestins (such as β-arrestin 1 and β-arrestin 2) to mediate diverse cellular and physiological responses. Biased signaling allows for the specific activation of certain pathways from the full range of receptors' signaling capabilities. Targeting more variable allosteric sites, which are spatially different from the highly conserved orthosteric sites, represents a novel approach in biased GPCR drug discovery, leading to innovative strategies for targeting GPCRs. Notably, the emergence of cryptic allosteric sites on GPCRs has expanded the repertoire of available drug targets and improved receptor subtype selectivity. Here, we conduct a summary of recent progress in the structural determination of cryptic allosteric sites on GPCRs and elucidate the biased signaling mechanisms induced by allosteric modulators. Additionally, we discuss means to identify cryptic allosteric sites and design biased allosteric modulators based on cryptic allosteric sites through structure-based drug design, which is an advanced pharmacotherapeutic approach for treating GPCR-associated diseases.

mTOR Variants Activation Discovers PI3K-like Cryptic Pocket, Expanding Allosteric, Mutant-Selective Inhibitor Designs

mTOR plays a crucial role in PI3K/AKT/mTOR signaling. We hypothesized that mTOR activation mechanisms driving oncogenesis can advise effective therapeutic designs. To test this, we combined cancer genomic analysis with extensive molecular dynamics simulations of mTOR oncogenic variants. We observed that conformational changes within mTOR kinase domain are associated with multiple mutational activation events. The mutations disturb the α-packing formed by the kαAL, kα3, kα9, kα9b, and kα10 helices in the kinase domain, creating cryptic pocket. Its opening correlates with opening of the catalytic cleft, including active site residues realignment, favoring catalysis. The cryptic pocket created by disrupted α-packing coincides with the allosteric pocket in PI3Kα can be harmoniously fitted by the PI3Kα allosteric inhibitor RLY-2608, suggesting that analogous drugs designed based on RLY-2608 can restore the packed α-structure, resulting in mTOR inactive conformation. Our results exemplify that knowledge of detailed kinase activation mechanisms can inform innovative allosteric inhibitor development.

Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT

Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based methods like molecular docking are too slow to allow for broad proteome-scale screens, limiting their application in screening for off-target effects or new molecular mechanisms. Recently, vector-based methods using protein language models (PLMs) have emerged as a complementary approach that bypasses explicit 3D structure modeling. Here, we develop SPRINT, a vector-based approach for screening entire chemical libraries against whole proteomes for DTIs and novel mechanisms of action. SPRINT improves on prior work by using a self-attention based architecture and structure-aware PLMs to learn a co-embedding space for drugs and targets, enabling efficient binder prediction, search, and retrieval. SPRINT achieves SOTA enrichment factors in virtual screening on LIT-PCBA, DTI classification benchmarks, and binding affinity prediction benchmarks, while providing interpretability in the form of residue-level attention maps. In addition to being both accurate and interpretable, SPRINT is ultra-fast: querying the whole human proteome against the ENAMINE Real Database (6.7B drugs) for the 100 most likely binders per protein takes 16 minutes. SPRINT promises to enable virtual screening at an unprecedented scale, opening up new opportunities for in silico drug repurposing and development. SPRINT is available on the web as ColabScreen: https://bit.ly/colab-screen.

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

CryptoBench: cryptic protein-ligand binding sites dataset and benchmark

MOTIVATION

Structure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations.

RESULTS

To advance the development in this domain, we introduce CryptoBench, a benchmark dataset tailored for training and evaluating novel CBS prediction methodologies. CryptoBench is constructed upon a large collection of apo-holo protein pairs, grouped by UniProtID, clustered by sequence identity, and filtered to contain only structures with substantial structural change in the binding site. CryptoBench comprises 1107 structures with predefined cross-validation splits, making it the most extensive CBS dataset to date. To establish a performance baseline, we measured the predictive power of sequence- and structure-based CBS residue prediction methods using the benchmark. We selected PocketMiner as the state-of-the-art representative of the structure-based methods for CBS detection, and P2Rank, a widely-used structure-based method for general binding site prediction that is not specifically tailored for cryptic sites. For sequence-based approaches, we trained a neural network to classify binding residues using protein language model embeddings. Our sequence-based approach outperformed PocketMiner and P2Rank across key metrics, including area under the curve, area under the precision-recall curve, Matthew's correlation coefficient, and F1 scores. These results provide baseline benchmark results for future CBS and potentially also non-CBS prediction endeavors, leveraging CryptoBench as the foundational platform for further advancements in the field.

AVAILABILITY AND IMPLEMENTATION

The CryptoBench dataset, including the benchmark model, is available on Open Science Framework-https://osf.io/pz4a9/. The code and tutorial are available at the GitHub repository-https://github.com/skrhakv/CryptoBench/.

Enhanced identification of small molecules binding to hnRNPA1 via cryptic pockets mapping coupled with X-Ray fragment screening

The human heterogeneous nuclear ribonucleoprotein (hnRNP) A1 is a prototypical RNA-binding protein essential in regulating a wide range of post-transcriptional events in cells. As a multifunctional protein with a key role in RNA metabolism, deregulation of its functions has been linked to neurodegenerative diseases, tumour aggressiveness and chemoresistance, which has fuelled efforts to develop novel therapeutics that modulates its RNA binding activities. Here, using a combination of Molecular Dynamics (MD) simulations and graph neural network pockets predictions, we showed that hnRNPA1 N-terminal RNA binding domain (UP1) contains several cryptic pockets capable of binding small molecules. To identify chemical entities for development of potent drug candidates and experimentally validate identified druggable hotspots, we carried out a large fragment screening on UP1 protein crystals. Our screen identified 36 hits which extensively samples UP1 functional regions involved in RNA recognition and binding, as well as mapping hotspots onto novel protein interaction surfaces. We observed a wide range of ligand-induced conformational variation, by stabilisation of dynamic protein regions. Our high-resolution structures, the first of an hnRNP in complex with a fragment or small molecule, provides rapid routes for the rational development of a range of different inhibitors and chemical tools for studying molecular mechanisms of hnRNPA1 mediated splicing regulation.

Methylophiopogonanone A Inhibits Ferroptosis in H9c2 Cells: An Experimental and Molecular Simulation Study

In this study, homoisoflavone methylophiopogonanone A (MOA) was investigated for its inhibitory effect on ferroptosis of H9c2 cells using a set of cellular assays, such as BODIPY-probed and H2DCFDA-probed flow cytometry analyses, cell counting kit-8 analysis (CCK-8), and lactate dehydrogenase (LDH) release analysis. All these cellular assays adopted Fer-1 as the positive control. Subsequently, MOA and Fer-1 were subjected to two antioxidant assays, i.e., 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide radical (PTIO•)-scavenging and 2,2'-azinobis(3-ethylbenzo-thiazoline-6-sulfonic acid radical (ABTS•+)-scavenging. Finally, MOA, along with Fer-1, were systematically analyzed for molecular docking and dynamics simulations using a set of software tools. The experimental results revealed that MOA could inhibit ferroptosis of H9c2 cells but did not effectively scavenge PTIO• and ABTS•+ free radicals. Two molecular simulation methods or algorithms suggested that MOA possessed similar binding affinity and binding free energy (∆Gbind) to Fer-1. Visual analyses indicated various hydrophobic interactions between MOA and one of the seven enzymes, including superoxide dismutase (SOD), dihydroorotate dehydrogenase (DHODH), ferroportin1 (FPN), ferroptosis suppressor protein 1 (FSP1), glutathione peroxidase 4 (GPX4), nicotinamide adenine dinucleotide phosphate (NADPH), and solute carrier family 7 member 11 (SLC7A11). Based on these experimental and molecular simulation results, it is concluded that MOA, a homoisoflavonoid with meta-di-OHs, can inhibit ferroptosis in H9c2 cells. Its inhibitory effect is mainly attributed to the regulation of enzymes rather than direct free radical scavenging. The regulation of enzymes primarily depends on hydrophobic interactions rather than H-bond formation. During the process, flexibility around position 9 allows MOA to adjust to the enzyme binding site. All these findings provide foundational information for developing MOA and its derivatives as potential drugs for myocardial diseases.

AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins are defined by the structural topology of the fold and favor conserved conformational motions driven by soft modes. Our findings provide evidence that AlphaFold2 combined with randomized alanine sequence masking can yield accurate and consistent results in predicting moderate conformational adjustments between apo and holo states, especially for proteins with localized changes upon ligand binding. For large hinge-like domain movements, the proposed approach can predict functional conformations characteristic of both apo and ligand-bound holo ensembles in the absence of ligand information. These results are relevant for using this AlphaFold adaptation for probing conformational selection mechanisms according to which proteins can adopt multiple conformations, including those that are competent for ligand binding. The results of this study indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and the detection of high-energy conformations. By incorporating a wider variety of protein structures in training datasets, including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, several approaches have been developed that make use of information determined from the 3D structure for a variety of downstream tasks. However, methods for predicting binding affinity that do consider protein structure generally do not take full advantage of such 3D structural protein information, often using such information only to define nearest-neighbor graphs based on inter-residue or inter-atomic distances. Here, we present a joint architecture that we call CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity) that makes use of an SE(3)-equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations, and we further augment these representations by incorporating an attention-based mechanism by which individual residues in a protein can attend to atoms in a ligand and vice-versa to improve interpretability. In this manner, we show that using equivariant graph neural networks in our architecture enables CASTER-DTA to approach and exceed state-of-the-art performance in predicting drug-target affinity without the inclusion of external information, such as protein language model embeddings. We do so on the Davis and KIBA datasets, common benchmarks for predicting drug-target affinity. We also discuss future steps to further improve performance.

Accurate and transferable drug-target interaction prediction with DrugLAMP

MOTIVATION

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.

RESULTS

We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules.

AVAILABILITY AND IMPLEMENTATION

Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources.

Revisiting the Plasmodium falciparum druggable genome using predicted structures and data mining

The identification of novel drug targets for the purpose of designing small molecule inhibitors is key component to modern drug discovery. In malaria parasites, discoveries of antimalarial targets have primarily occurred retroactively by investigating the mode of action of compounds found through phenotypic screens. Although this method has yielded many promising candidates, it is time- and resource-consuming and misses targets not captured by existing antimalarial compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction and data mining, we systematically assessed the Plasmodium falciparum genome for proteins amenable to target-based drug discovery, identifying 867 candidate targets with evidence of small molecule binding and blood stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 67 high priority candidates. This study also provides a genome-wide data resource and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites

In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallography and cryo-electron microscopy have been used to unravel these structures, but they are often challenging, time-consuming and costly. Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. Nature 2021, 596, 583. Lane, T. J. Nature Methods 2023, 20, 170. Kryshtafovych, A., et al. Proteins: Structure, Function and Bioinformatics 2021, 89, 1607). This study focuses on predicting the dynamic changes that proteins undergo upon ligand binding, specifically when they bind to allosteric sites, i.e. a pocket different from the active site. Allosteric modulators are particularly important for drug discovery, as they open new avenues for designing drugs that can target proteins more effectively and with fewer side effects (Nussinov, R.; Tsai, C. J. Cell 2013, 153, 293). To study this, we curated a data set of 578 X-ray structures comprised of proteins displaying orthosteric and allosteric binding as well as a general framework to evaluate deep learning-based structure prediction methods. Our findings demonstrate the potential and current limitations of deep learning methods, such as AlphaFold2 (Jumper, J., et al. Nature 2021, 596, 583), NeuralPLexer (Qiao, Z., et al. Nat Mach Intell 2024, 6, 195), and RoseTTAFold All-Atom (Krishna, R., et al. Science 2024, 384, eadl2528) to predict not just static protein structures but also the dynamic conformational changes. Herein we show that predicting the allosteric induce-fit conformation still poses a challenge to deep learning methods as they more accurately predict the orthosteric bound conformation compared to the allosteric induce fit conformation. For AlphaFold2, we observed that conformational diversity, and sampling between the apo and holo state could be increased by modifying the MSA depth, but this did not enhance the ability to generate conformations close to the allosteric induced-fit conformation. To further support advancements in protein structure prediction field, the curated data set and evaluation framework are made publicly available.

CosolvKit: a Versatile Tool for Cosolvent MD Preparation and Analysis

Cosolvent molecular dynamics (MDs) are an increasingly popular form of simulations where small molecule cosolvents are added to water-solvated protein systems. These simulations can perform diverse target characterization tasks, including cryptic and allosteric pocket identification and pharmacophore profiling and supplement suites of enhanced sampling methods to explore protein conformational landscapes. The behavior of these systems is tied to the cosolvents used, so the ability to define diverse and complex mixtures is critical in dictating the outcome of the simulations. However, existing methods for preparing cosolvent simulations only support a limited number of predefined cosolvents and concentrations. Here, we present CosolvKit, a tool for the preparation and analysis of systems composed of user-defined cosolvents and concentrations. This tool is modular, supporting the creation of files for multiple MD engines, as well as direct access to OpenMM simulations, and offering access to a variety of generalizable small-molecule force fields. To the best of our knowledge, CosolvKit represents the first generalized approach for the construction of these simulations.

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models

Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an diffusion framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiff's potential to advance computational drug discovery and protein engineering.

Which cryptic sites are feasible drug targets?

Cryptic sites can expand the space of druggable proteins, but the potential usefulness of such sites needs to be investigated before any major effort. Given that the binding pockets are not formed, the druggability of such sites is not well understood. The analysis of proteins and their ligands shows that cryptic sites that are formed primarily by the motion of side chains moving out of the pocket to enable ligand binding generally do not bind drug-sized molecules with sufficient potency. By contrast, sites that are formed by loop or hinge motion are potentially valuable drug targets. Arguments are provided to explain the underlying causes in terms of classical enzyme inhibition theory and the kinetics of side chain motion and ligand binding.

GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction.Scientific contributionThe prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs .

Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types-to 'map' variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes.

Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery

In the past several years there has been rapid adoption of artificial intelligence (AI) and machine learning (ML) tools for drug discovery. In this Microperspective, we comment on recent AI/ML applications to the discovery of allosteric modulators, focusing on breakthroughs with AlphaFold, structure-based drug discovery (SBDD), and medicinal chemistry applications. We discuss how these technologies are facilitating drug discovery and the remaining challenges to identify allosteric binding sites and ligands.

Toward Unveiling Putative Binding Sites of Interleukin-33: Insights from Mixed-Solvent Molecular Dynamics Simulations of the Interleukin-1 Family

The interleukin (IL)-1 family is a major proinflammatory cytokine family, ranging from the well-studied IL-1s to the most recently discovered IL-33. As a new focus, IL-33 has attracted extensive research for its crucial immunoregulatory roles, leading to the development of notable monoclonal antibodies as clinical candidates. Efforts to develop small molecules disrupting IL-33/ST2 interaction remain highly desired but encounter challenges due to the shallow and featureless interfaces. The information from relative cytokines has shown that traditional binding site identification methods still struggle in mapping cryptic sites, necessitating dynamic approaches to uncover druggable pockets on IL-33. Here, we employed mixed-solvent molecular dynamics (MixMD) simulations with diverse-property probes to map the hotspots of IL-33 and identify potential binding sites. The protocol was first validated using the known binding sites of two IL-1 family members and then applied to the structure of IL-33. Our simulations revealed several binding sites and proposed side-chain rearrangements essential for the binding of a known inhibitor, aligning well with experimental NMR findings. Further microsecond-time scale simulations of this IL-33-protein complex unveiled distinct binding modes with varying occurrences. These results could facilitate future efforts in developing ligands to target challenging flexible pockets of IL-33 and IL-1 family cytokines in general.

AHoJ-DB: A PDB-wide Assignment of apo & holo Relationships Based on Individual Protein-Ligand Interactions

A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein-ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data. Availability: www.apoholo.cz/db.

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles

Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, "solved" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Comparative CO2 and SiO2 hydratase activity of an enzyme from the siliceous demosponge Suberitesdomuncula

Silicase, an enzyme that catalyzes the hydrolysis of silicon-oxygen bonds, is a crucial player in breaking down silicates into silicic acid, particularly in organisms like aquatic sponges with siliceous skeletons. Despite its significance, our understanding of silicase remains limited. This study comprehensively examines silicase from the demosponge Suberites domuncula, focusing on its kinetics toward CO2 as a substrate, as well as its silicase and esterase activity. It investigates inhibition and activation profiles with a range of inhibitors and activators belonging to various classes. By comparing its esterase activity to human carbonic anhydrase II, we gain insights into its enzymatic properties. Moreover, we investigate silicase's inhibition and activation profiles, providing valuable information for potential applications. We explore the evolutionary relationship of silicase with related enzymes, revealing potential functional roles in biological systems. Additionally, we propose a biochemical mechanism through three-dimensional modeling, shedding light on its catalytic mechanisms and structural features for both silicase activity and CO2 hydration. We highlight nature's utilization of enzymatic expertise in silica metabolism. This study enhances our understanding of silicase and contributes to broader insights into ecosystem functioning and Earth's geochemical cycles, emphasizing the intricate interplay between biology and the environment.

Exploring the Application of SiteMap and Site Finder for Focused Cryptic Pocket Identification

The characterization of cryptic pockets has been elusive, despite substantial efforts. Computational modeling approaches, such as molecular dynamics (MD) simulations, can provide atomic-level details of binding site motions and binding pathways. However, the time scale that MD can achieve at a reasonable cost often limits its application for cryptic pocket identification. Enhanced sampling techniques can improve the efficiency of MD simulations by focused sampling of important regions of the protein, but prior knowledge of the simulated system is required to define the appropriate coordinates. In the case of a novel, unknown cryptic pocket, such information is not available, limiting the application of enhanced sampling techniques for cryptic pocket identification. In this work, we explore the ability of SiteMap and Site Finder, widely used commercial packages for pocket identification, to detect focus points on the protein and further apply other advanced computational methods. The information gained from this analysis enables the use of computational modeling, including enhanced MD sampling techniques, to explore potential cryptic binding pockets suggested by SiteMap and Site Finder. Here, we examined SiteMap and Site Finder results on 136 known cryptic pockets from a combination of the PocketMiner dataset (a recently curated set of cryptic pockets), the Cryptosite Set (a classic set of cryptic pockets), and Natural killer group 2D (NKG2D, a protein target where a cryptic pocket is confirmed). Our findings demonstrate the application of existing, well-studied tools in efficiently mapping potential regions harboring cryptic pockets.

The Malaria Box molecules: a source for targeting the RBD and NTD cryptic pocket of the spike glycoprotein in SARS-CoV-2

CONTEXT

SARS-CoV-2, responsible for COVID-19, has led to over 500 million infections and more than 6 million deaths globally. There have been limited effective treatments available. The study aims to find a drug that can prevent the virus from entering host cells by targeting specific sites on the virus's spike protein.

METHOD

We examined 13,397 compounds from the Malaria Box library against two specific sites on the spike protein: the receptor-binding domain (RBD) and a predicted cryptic pocket. Using virtual screening, molecular docking, molecular dynamics, and MMPBSA techniques, they evaluated the stability of two compounds. TCMDC-124223 showed high stability and binding energy in the RBD, while TCMDC-133766 had better binding energy in the cryptic pocket. The study also identified that the interacting residues are conserved, which is crucial for addressing various virus variants. The findings provide insights into the potential of small molecules as drugs against the spike protein.

Leveraging Bidirectional Nature of Allostery To Inhibit Protein-Protein Interactions (PPIs): A Case Study of PCSK9-LDLR Interaction

The protein PCSK9 (proprotein convertase subtilisin/Kexin type 9) negatively regulates the recycling of LDLR (low-density lipoprotein receptor), leading to an elevated plasma level of LDL. Inhibition of PCSK9-LDLR interaction has emerged as a promising therapeutic strategy to manage hypercholesterolemia. However, the large interaction surface area between PCSK9 and LDLR makes it challenging to identify a small molecule competitive inhibitor. An alternative strategy would be to identify distal cryptic sites as targets for allosteric inhibitors that can remotely modulate PCSK9-LDLR interaction. Using several microseconds long molecular dynamics (MD) simulations, we demonstrate that on binding with LDLR, there is a significant conformational change (population shift) in a distal loop (residues 211-222) region of PCSK9. Consistent with the bidirectional nature of allostery, we establish a clear correlation between the loop conformation and the binding affinity with LDLR. Using a thermodynamic argument, we establish that the loop conformations predominantly present in the apo state of PCSK9 would have lower LDLR binding affinity, and they would be potential targets for designing allosteric inhibitors. We elucidate the molecular origin of the allosteric coupling between this loop and the LDLR binding interface in terms of the population shift in a set of salt bridges and hydrogen bonds. Overall, our work provides a general strategy toward identifying allosteric hotspots: compare the conformational ensemble of the receptor between the apo and bound states of the protein and identify distal conformational changes, if any. The inhibitors should be designed to bind and stabilize the apo-specific conformations.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Engineering extracellular electron transfer pathways of electroactive microorganisms by synthetic biology for energy and chemicals production

The excessive consumption of fossil fuels causes massive emission of CO2, leading to climate deterioration and environmental pollution. The development of substitutes and sustainable energy sources to replace fossil fuels has become a worldwide priority. Bio-electrochemical systems (BESs), employing redox reactions of electroactive microorganisms (EAMs) on electrodes to achieve a meritorious combination of biocatalysis and electrocatalysis, provide a green and sustainable alternative approach for bioremediation, CO2 fixation, and energy and chemicals production. EAMs, including exoelectrogens and electrotrophs, perform extracellular electron transfer (EET) (i.e., outward and inward EET), respectively, to exchange energy with the environment, whose rate determines the efficiency and performance of BESs. Therefore, we review the synthetic biology strategies developed in the last decade for engineering EAMs to enhance the EET rate in cell-electrode interfaces for facilitating the production of electricity energy and value-added chemicals, which include (1) progress in genetic manipulation and editing tools to achieve the efficient regulation of gene expression, knockout, and knockdown of EAMs; (2) synthetic biological engineering strategies to enhance the outward EET of exoelectrogens to anodes for electricity power production and anodic electro-fermentation (AEF) for chemicals production, including (i) broadening and strengthening substrate utilization, (ii) increasing the intracellular releasable reducing equivalents, (iii) optimizing c-type cytochrome (c-Cyts) expression and maturation, (iv) enhancing conductive nanowire biosynthesis and modification, (v) promoting electron shuttle biosynthesis, secretion, and immobilization, (vi) engineering global regulators to promote EET rate, (vii) facilitating biofilm formation, and (viii) constructing cell-material hybrids; (3) the mechanisms of inward EET, CO2 fixation pathway, and engineering strategies for improving the inward EET of electrotrophic cells for CO2 reduction and chemical production, including (i) programming metabolic pathways of electrotrophs, (ii) rewiring bioelectrical circuits for enhancing inward EET, and (iii) constructing microbial (photo)electrosynthesis by cell-material hybridization; (4) perspectives on future challenges and opportunities for engineering EET to develop highly efficient BESs for sustainable energy and chemical production. We expect that this review will provide a theoretical basis for the future development of BESs in energy harvesting, CO2 fixation, and chemical synthesis.

Protein dynamics underlying allosteric regulation

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.