Energy Landscape Topography Reveals the Underlying Link Between Binding Specificity and Activity of Enzymes

Enhancing the activity and succinyl-CoA specificity of 3-ketoacyl-CoA thiolase Tfu_0875 through rational binding pocket engineering

The 3-ketoacyl-CoA thiolase is the rate-limiting enzyme for linear dicarboxylic acids production. However, the promiscuous substrate specificity and suboptimal catalytic performance have restricted its application. Here we present both biochemical and structural analyses of a high-efficiency 3-ketoacyl-CoA thiolase Tfu_0875. Notably, Tfu_0875 displayed heightened activity and substrate specificity for succinyl-CoA, a key precursor in adipic acid production. To enhance its performance, a deep learning approach (DLKcat) was employed to identify effective mutants, and a computational strategy, known as the greedy accumulated strategy for protein engineering (GRAPE), was used to accumulate these effective mutants. Among the mutants, Tfu_0875N249W/L163H/E217L exhibited the highest specific activity (320% of wild-type Tfu_0875), the greatest catalytic efficiency (kcat/KM = 1.00 min-1mM-1), the highest succinyl-CoA specificity (KM = 0.59 mM, 28.1% of Tfu_0875) and dramatically reduced substrate binding energy (-30.25 kcal mol-1v.s. -15.94 kcal mol-1). A structural comparison between Tfu_0875N249W/L163H/E217L and the wild type Tfu_0875 revealed that the increased interaction between the enzyme and succinyl-CoA was the primary reason for the enhanced enzyme activity. This interaction facilitated rapid substrate anchoring and stabilization. Furthermore, a reduced binding pocket volume improved substrate specificity by enhancing the complementarity between the binding pocket and the substrate in stereo conformation. Finally, our rationally designed mutant, Tfu_0875N249W/L163H/E217L, increased the adipic acid titer by 1.35-fold compared to the wild type Tfu_0875 in shake flask. The demonstrated enzymatic methods provide a promising enzyme variant for the adipic acid production. The above effective substrate binding pocket engineering strategy can be beneficial for the production of other industrially competitive biobased chemicals when be applied to other thiolases.

In silico molecular docking, molecular dynamics, ADMET analysis of fucoidan against receptor frizzled-8 and coreceptor LRP6 in Wnt/β-Catenin pathway and in vitro analysis of fucoidan extract from Sargassum echinocarpum as β-catenin inhibitor in breast cancer cell line (MCF-7)

This study aimed to investigate the effect of fucoidan on the Wnt/β-Catenin pathway using both in-silico molecular docking, molecular dynamics, ADMET analysis (in frizzled-8 receptor and LRP6 coreceptor) and in-vitro experiments using MCF-7 breast cancer cells. Through the molecular docking analysis, the binding energies on the frizzled-8 receptor were -5.6, -5.1, -9.4, and -8.8 kcal/mol, respectively. Meanwhile, those on the LRP6 receptor, were -7.3, -6.2, -10.0, and -9.8 kcal/mol, respectively. The results showed that fucoidan had a favorable binding affinity for both receptors. Furthermore, it was discovered to reduce the interaction and binding affinity between Wnt agonists to frizzled-8 and LRP6 receptors. This reduction was reflected in the change in the binding energy of the fucoidan-Wnt agonist-frizzled 8 and fucoidan-Wnt agonist-LRP6 complexes, which exhibited decreases of -7.0 kcal/mol and -7.8 kcal/mol, respectively. Fucoidan was found stable in complexes with frizzled-8 receptor and co-receptor LRP6. ADMET study showed it's non-carcinogenic and can be distributed in the body. Fucoidan effectively inhibited β-catenin production, a critical factor in the Wnt/β-catenin pathway. The MCF-7 breast cancer cells were treated with fucoidan extract from S. echinocarpum at incubation times of 24, 48, and 72 h, resulting in a reduction of β-catenin levels by 95.19%, 83.88%, and 80.88%, respectively. Fucoidan also shows no significant difference in value compared to fucoidan standard (F. vesiculosus) and doxorubicin. Fucoidan exhibited antiproliferative effects against breast cancer cells, specifically through its modulation of the Wnt/β-Catenin pathway, and held great potential as an herbal anticancer agent.Communicated by Ramaswamy H. Sarma.

Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design

The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer numerous prerequisite binding sites that may be visited by an inhibitor during migration from a bulk solution to the destination site. Drug design can use these prerequisite sites as new structural targets. However, identifying these ephemeral sites is challenging. Here, we introduce a new method called NetBinder for the systematic identification and classification of prerequisite binding sites at atomic resolution. NetBinder is based on atomistic simulations of the full inhibitor binding process and provides a networking framework on which to select the most important binding modes and uncover the entire binding mechanism, including previously undiscovered events. NetBinder was validated by a study of the binding mechanism of blebbistatin (a potent inhibitor) to myosin 2 (a promising target for cancer chemotherapy). Myosin 2 is a good test enzyme because, like other potential targets, it has a long internal binding cavity that provides blebbistatin with numerous potential prerequisite binding sites. The mechanism proposed by NetBinder of myosin 2 structural changes during blebbistatin binding shows excellent agreement with experimentally determined binding sites and structural changes. While NetBinder was tested on myosin 2, it may easily be adopted to other proteins with long internal cavities, such as G-protein-coupled receptors or ion channels, the most popular current drug targets. NetBinder provides a new paradigm for drug design by a network-based elucidation of binding mechanisms at an atomic resolution.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Insight into interaction mechanism between theaflavin-3-gallate and α-glucosidase using spectroscopy and molecular docking analysis

To elucidate the α-glucosidase (α-GC) inhibitory mechanism of theaflavin-3-gallate (TF-3-G), their interaction mechanism was investigated using spectroscopy and molecular docking analysis. The inhibition ratio of TF-3-G against α-GC was determined to be 92.3%. Steady fluorescence spectroscopy showed that TF-3-G effectively quenched the intrinsic fluorescence of α-GC through static quenching, forming a stable complex through hydrophobic interactions. Formation of the TF-3-G/α-GC complex was also confirmed by resonance light scattering spectroscopy. Synchronous fluorescence spectroscopy and circular dichroism spectroscopy indicated that the secondary structure of α-GC was changed by TF-3-G. Molecular docking was used to simulate TF-3-G/α-GC complex formation, showing that TF-3-G might be inserted into the hydrophobic region around the active site of ɑ-GC, and bind with the catalytic Asp215 and Asp352 residues. The ɑ-GC inhibitory mechanism of TF-3-G was mainly attributed to the change in ɑ-GC secondary structure caused by the complex formation. PRACTICAL APPLICATIONS: α-Glucosidase (α-GC) can hydrolyze the glycosidic bonds of starch and oligosaccharides in food and release glucose. Therefore, the inhibition of α-GC activity has been used to treat postprandial hyperglycemia and type 2 diabetes mellitus. Theaflavin-3-gallate (TF-3-G), a flavonoid found in the fermentation products of black tea, exhibits strong inhibition of α-GC activity. However, the α-GC inhibitory mechanism of TF-3-G is unclear. This study aids understanding of this mechanism, and proposed a possibly basic theory for improving the medicinal value of TF-3-G in diabetes therapy.

Overcoming the Bottleneck of the Enzymatic Cycle by Steric Frustration

The enormous catalytic power of natural enzymes relies on the ability to overcome the bottleneck event in the enzymatic cycle, yet the underlying physical mechanisms are not fully understood. Here, by performing molecular simulations of the whole enzymatic cycle for a model multisubstrate enzyme with a dynamic energy landscape model, we show that multisubstrate enzymes can utilize steric frustration to facilitate the rate-limiting product-release step. During the enzymatic cycles, the bottleneck product is actively squeezed out by the binding of a new substrate at the neighboring site through the population of a substrate-product cobound complex, in which the binding pockets are frustrated due to steric incompatibility. Such steric frustration thereby enables an active mechanism of product release driven by substrate-binding energy, facilitating the enzymatic cycle.

Examining a Thermodynamic Order Parameter of Protein Folding

Dimensionality reduction with a suitable choice of order parameters or reaction coordinates is commonly used for analyzing high-dimensional time-series data generated by atomistic biomolecular simulations. So far, geometric order parameters, such as the root mean square deviation, fraction of native amino acid contacts, and collective coordinates that best characterize rare or large conformational transitions, have been prevailing in protein folding studies. Here, we show that the solvent-averaged effective energy, which is a thermodynamic quantity but unambiguously defined for individual protein conformations, serves as a good order parameter of protein folding. This is illustrated through the application to the folding-unfolding simulation trajectory of villin headpiece subdomain. We rationalize the suitability of the effective energy as an order parameter by the funneledness of the underlying protein free energy landscape. We also demonstrate that an improved conformational space discretization is achieved by incorporating the effective energy. The most distinctive feature of this thermodynamic order parameter is that it works in pointing to near-native folded structures even when the knowledge of the native structure is lacking, and the use of the effective energy will also find applications in combination with methods of protein structure prediction.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Effects of Glycosylation on the Enzymatic Activity and Mechanisms of Proteases

Posttranslational modifications are an important feature of most proteases in higher organisms, such as the conversion of inactive zymogens into active proteases. To date, little information is available on the role of glycosylation and functional implications for secreted proteases. Besides a stabilizing effect and protection against proteolysis, several proteases show a significant influence of glycosylation on the catalytic activity. Glycans can alter the substrate recognition, the specificity and binding affinity, as well as the turnover rates. However, there is currently no known general pattern, since glycosylation can have both stimulating and inhibiting effects on activity. Thus, a comparative analysis of individual cases with sufficient enzyme kinetic and structural data is a first approach to describe mechanistic principles that govern the effects of glycosylation on the function of proteases. The understanding of glycan functions becomes highly significant in proteomic and glycomic studies, which demonstrated that cancer-associated proteases, such as kallikrein-related peptidase 3, exhibit strongly altered glycosylation patterns in pathological cases. Such findings can contribute to a variety of future biomedical applications.

Protein dynamics: Conformational footprints

The mechanisms by which proteins evolve new functions can be slow and mysterious. Comprehensive structural analysis of enzyme variants reveal how gradual enrichments of pre-existing populations with the right productive dynamics for new functions can accomplish this aim.