Long-timescale molecular dynamics simulations of protein structure and function

In silico identification of potential protein kinase C alpha inhibitors from phytochemicals from IMPPAT database for anticancer therapeutics: a virtual screening approach

Protein Kinase C alpha (PKCα) is a critical signaling molecule that plays a crucial role in various physiological processes, including cell growth, differentiation, and survival. Over the years, there has been a growing interest in targeting PKCα as a promising drug target for the treatment of various diseases, including cancer. Targeting PKCα can, therefore, serve as a potential strategy to prevent cancer progression and enhance the efficacy of conventional anticancer therapies. We conducted a systematic search for promising compounds for their anticancer potential that target PKCα using natural compounds from the IMPPAT database. The initial compounds were screened through various tests, including analysis of their physical and chemical properties, PAINS filter, ADMET analysis, PASS analysis, and specific interaction analysis. We selected those that showed high binding affinity and specificity to PKCα from the screened compounds, and we further analyzed them using molecular dynamics simulations (MDS) and principal component analysis (PCA). Various systematic parameters from the MDS analyses suggested that the protein-ligand complexes were stabilized throughout the simulation trajectories of 100 nanoseconds (ns). Our findings indicated that compounds Nicandrenone and Withaphysalin D bind to PKCα with high stability and affinity, making them potential candidates for further research in cancer therapeutics innovation in clinical contexts.Communicated by Ramaswamy H. Sarma.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib's action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors.Communicated by Ramaswamy H. Sarma.

Single Molecule Thermodynamic Penalties Applied to Enzymes by Whispering Gallery Mode Biosensors

Optical microcavities, particularly whispering gallery mode (WGM) microcavities enhanced by plasmonic nanorods, are emerging as powerful platforms for single-molecule sensing. However, the impact of optical forces from the plasmonic near field on analyte molecules is inadequately understood. Using a standard optoplasmonic WGM single-molecule sensor to monitor two enzymes, both of which undergo an open-to-closed-to-open conformational transition, the work done on an enzyme by the WGM sensor as atoms of the enzyme move through the electric field gradient of the plasmonic hotspot during conformational change has been quantified. As the work done by the sensor on analyte enzymes can be modulated by varying WGM intensity, the WGM microcavity system can be used to apply free energy penalties to regulate enzyme activity at the single-molecule level. The findings advance the understanding of optical forces in WGM single-molecule sensing, potentially leading to the capability to precisely manipulate enzyme activity at the single-molecule level through tailored optical modulation.

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

Search for novel Plasmodium falciparum PfATP4 inhibitors from the MMV Pandemic Response Box through a virtual screening approach

Owing to its life cycle involving multiple hosts and species-specific biological complexities, a vaccine against Plasmodium, the causative agent of Malaria remains elusive. This makes chemotherapy the only viable means to address the clinical manifestations and spread of this deadly disease. However, rapid surge in antimalarial resistance poses significant challenges to our efforts to eliminate Malaria since the best drug available to-date; Artemisinin and its combinations are also rapidly losing efficacy. Sodium ATPase (PfATP4) of Plasmodium has been recently explored as a suitable target for new antimalarials such as Cipargamin. Prior studies showed that multiple compounds from the Medicines for Malaria Venture (MMV) chemical libraries were efficient PfATP4 inhibitors. In this context, we undertook a structure- based virtual screening approach combined to Molecular Dynamic (MD) simulations to evaluate whether new molecules with binding affinity towards PfATP4 could be identified from the Pandemic Response Box (PRB), a 400-compound library of small molecules launched in 2019 by MMV. Our analysis identified new molecules from the PRB library that showed affinity for distinct binding sites including the previously known G358 site, several of which are clinically used anti-bacterial (MMV1634383, MMV1634402), antiviral (MMV010036, MMV394033) or antifungal (MMV1634494) agents. Therefore, this study highlights the possibility of exploiting PRB molecules against Malaria through abrogation of PfATP4 activity.Communicated by Ramaswamy H. Sarma.

Functional characterization, structural basis, and protein engineering of a rare flavonoid 2'-O-glycosyltransferase from Scutellaria baicalensis

Glycosylation is an important post-modification reaction in plant secondary metabolism, and contributes to structural diversity of bioactive natural products. In plants, glycosylation is usually catalyzed by UDP-glycosyltransferases. Flavonoid 2'-O-glycosides are rare glycosides. However, no UGTs have been reported, thus far, to specifically catalyze 2'-O-glycosylation of flavonoids. In this work, UGT71AP2 was identified from the medicinal plant Scutellaria baicalensis as the first flavonoid 2'-O-glycosyltransferase. It could preferentially transfer a glycosyl moiety to 2'-hydroxy of at least nine flavonoids to yield six new compounds. Some of the 2'-O-glycosides showed noticeable inhibitory activities against cyclooxygenase 2. The crystal structure of UGT71AP2 (2.15 Å) was solved, and mechanisms of its regio-selectivity was interpreted by pK a calculations, molecular docking, MD simulation, MM/GBSA binding free energy, QM/MM, and hydrogen‒deuterium exchange mass spectrometry analysis. Through structure-guided rational design, we obtained the L138T/V179D/M180T mutant with remarkably enhanced regio-selectivity (the ratio of 7-O-glycosylation byproducts decreased from 48% to 4%) and catalytic efficiency of 2'-O-glycosylation (k cat/K m, 0.23 L/(s·μmol), 12-fold higher than the native). Moreover, UGT71AP2 also possesses moderate UDP-dependent de-glycosylation activity, and is a dual function glycosyltransferase. This work provides an efficient biocatalyst and sets a good example for protein engineering to optimize enzyme catalytic features through rational design.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm

Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We propose a new algorithm based on the shooting success rate, i.e., reactivity, measured as a function of a reduced set of collective variables (CVs). These variables are extracted with a machine learning approach directly from TPS simulations, using a multitask objective function. Iteratively, this workflow significantly improves the shooting efficiency without any prior knowledge of the process. In addition, the optimized CVs can be used with biased enhanced sampling methodologies to accurately reconstruct the free energy profiles. We tested the method on three different systems: a two-dimensional toy model, conformational transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk water. In the latter, we integrated our workflow with an active learning scheme to learn a reactive machine learning-based potential, which allowed us to study the mechanism and free energy profile with an ab initio-like accuracy.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. 2023, 19, 4355-4363).

Temporal Resolution of Activity-Related Solvation Dynamics in the TIM Barrel Enzyme Murine Adenosine Deaminase

Murine adenosine deaminase (mADA) is a prototypic system for studying the thermal activation of active site chemistry within the TIM barrel family of enzyme reactions. Previous temperature-dependent hydrogen deuterium exchange studies under various conditions have identified interconnected thermal networks for heat transfer from opposing protein-solvent interfaces to active site residues in mADA. One of these interfaces contains a solvent exposed helix-loop-helix moiety that presents the hydrophobic face of its long α-helix to the backside of bound substrate. Herein we pursue the time and temperature dependence of solvation dynamics at the surface of mADA, for comparison to established kinetic parameters that represent active site chemistry. We first created a modified protein devoid of native tryptophans with close to native kinetic behavior. Single site-specific tryptophan mutants were back inserted into each of the four positions where native tryptophans reside. Measurements of nanosecond fluorescence relaxation lifetimes and Stokes shift decays, that reflect time dependent environmental reoroganization around the photo-excited state of Trp*, display minimal temperature dependences. These regions serve as controls for the behavior of a new single tryptophan inserted into a solvent exposed region near the helix-loop-helix moiety located behind the bound substrate, Lys54Trp. This installed Trp displays a significantly elevated value for Ea ( k Stokes shift ) ; further, when Phe61 within the long helix positioned behind bound substrate is replaced by a series of aliphatic hydrophobic side chains, the trends in Ea ( k Stokes shift ) mirror the earlier reported impact of the same series of function-altering hydrophobic side chains on the activation energy of catalysis, Ea ( k cat ) .The reported experimental findings implicate a solvent initiated and rapid (>ns) protein restructuring that contributes to the enthalpic activation barrier to catalysis in mADA.

Toward transferable empirical valence bonds: Making classical force fields reactive

The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations.

Screening 2D Materials for Their Nanotoxicity toward Nucleic Acids and Proteins: An In Silico Outlook

Since the discovery of graphene, two-dimensional (2D) materials have been anticipated to demonstrate enormous potential in bionanomedicine. Unfortunately, the majority of 2D materials induce nanotoxicity via disruption of the structure of biomolecules. Consequently, there has been an urge to synthesize and identify biocompatible 2D materials. Before the cytotoxicity of 2D nanomaterials is experimentally tested, computational studies can rapidly screen them. Additionally, computational analyses can provide invaluable insights into molecular-level interactions. Recently, various "in silico" techniques have identified these interactions and helped to develop a comprehensive understanding of nanotoxicity of 2D materials. In this article, we discuss the key recent advances in the application of computational methods for the screening of 2D materials for their nanotoxicity toward two important categories of abundant biomolecules, namely, nucleic acids and proteins. We believe the present article would help to develop newer computational protocols for the identification of novel biocompatible materials, thereby paving the way for next-generation biomedical and therapeutic applications based on 2D materials.

Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.

Potentially active aspirin derivative to release nitric oxide: In-vitro, in-vivo and in-silico approaches

The series of newer salicylate derivatives incorporating nitroxy functionality were synthesized and evaluated for their potential effect in gastrointestinal (GI) related toxicity produced by aspirin. The synthesized compounds (5a-j) were subjected to %NO (nitric oxide) release study, in-vitro anti-inflammatory potential, % inhibition of carrageenan-induced paw edema and the obtained results were validated by in-silico studies including molecular docking, MD simulations and in-silico ADME (absorption, distribution, metabolism, and elimination) calculations. Compounds 5a (20.86 %) and 5g (18.20 %) displayed the highest percentage of NO release in all the tested compounds. Similarly, 5a and 5h were found to have (77.11 % and 79.53 %) &(78.56 % and 66.10 %) inhibition in carrageenan induced paw edema in animal mode which were relatively higher than ibuprofen (standard used). The obtained results were validated by molecular docking and MD simulations studies. The molecular docking study of 5a and 5h revealed that docking scores were also obtained in very close proximity of -8.35, -9.67 and -8.48 for ibuprofen, 5g and 5h respectively. In MD simulations studies, the calculated lower RMSD (root mean square deviation) values 2.8 Å and 5.6 Å for 5g and 5h, respectively indicated the stability of ligand-protein complexes. Similarly lower RSMF (root mean square fluctuation) values indicated the molecules remained in the active pocket throughout the entire MD simulations run. Further, in-silico ADME calculations were determined and all compounds obey the Lipinski's rule of five and it was predicted that these molecules would be orally active without any serious toxic effect.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

Trapping a non-cognate nucleotide upon initial binding for replication fidelity control in SARS-CoV-2 RNA dependent RNA polymerase

The RNA dependent RNA polymerase (RdRp) in SARS-CoV-2 is a highly conserved enzyme responsible for viral genome replication/transcription. To understand how the viral RdRp achieves fidelity control during such processes, here we computationally investigate the natural non-cognate vs. cognate nucleotide addition and selectivity during viral RdRp elongation. We focus on the nucleotide substrate initial binding (RdRp active site open) to the prechemical insertion (active site closed) of the RdRp. The current studies were first carried out using microsecond ensemble equilibrium all-atom molecular dynamics (MD) simulations. Due to the slow conformational changes (from open to closed) during nucleotide insertion and selection, enhanced or umbrella sampling methods have been further employed to calculate the free energy profiles of the nucleotide insertion. Our studies find notable stability of noncognate dATP and GTP upon initial binding in the active-site open state. The results indicate that while natural cognate ATP and Remdesivir drug analogue (RDV-TP) are biased toward stabilization in the closed state to facilitate insertion, the natural non-cognate dATP and GTP can be well trapped in off-path initial binding configurations and prevented from insertion so that to be further rejected. The current work thus presents the intrinsic nucleotide selectivity of SARS-CoV-2 RdRp for natural substrate fidelity control, which should be considered in antiviral drug design.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Development of the Integrated Computer Simulation Model of the Intracellular, Transmembrane, and Extracellular Domain of Platelet Integrin α IIb β 3 (Platelet Membrane Glycoprotein: GPIIb-IIIa)

Background  The structure and functions of the extracellular domain of platelet integrin α IIb β 3 (platelet membrane glycoprotein: GPIIb-IIIa) change substantially upon platelet activation. However, the stability of the integrated model of extracellular/transmembrane/intracellular domains of integrin α IIb β 3 with the inactive state of the extracellular domain has not been clarified. Methods  The integrated model of integrin α IIb β 3 was developed by combining the extracellular domain adopted from the crystal structure and the transmembrane and intracellular domain obtained by Nuclear Magnetic Resonace (NMR). The transmembrane domain was settled into the phosphatidylcholine (2-oleoyl-1-palmitoyl-sn-glycerol-3-phosphocholine (POPC)) lipid bilayer model. The position coordinates and velocity vectors of all atoms and water molecules around them were calculated by molecular dynamic (MD) simulation with the use of Chemistry at Harvard Macromolecular Mechanics force field in every 2 × 10 -15 seconds. Results  The root-mean-square deviations (RMSDs) of atoms constructing the integrated α IIb β 3 model apparently stabilized at approximately 23 Å after 200 ns of calculation. However, minor fluctuation persisted during the entire calculation period of 650 ns. The RMSDs of both α IIb and β 3 showed similar trends before 200 ns. The RMSD of β 3 apparently stabilized approximately at 15 Å at 400 ns with persisting minor fluctuation afterward, while the structural fluctuation in α IIb persisted throughout the 650 ns calculation period. Conclusion  In conclusion, the integrated model of the intracellular, transmembrane, and extracellular domain of integrin α IIb β 3 suggested persisting fluctuation even after convergence of MD calculation.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

SIGNIFICANCE

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Improving the thermostability of a novel PL-6 family alginate lyase by rational design engineering for industrial preparation of alginate oligosaccharides

Alginate is degraded into alginate oligosaccharides with various biological activities by enzymes. However, the thermostability of the enzyme limits its industrial application. In this study, a novel PL-6 alginate lyase, AlyRm6A from Rhodothermus marinus 4252 was expressed and characterized. In addition, an efficient comprehensive strategy was proposed, including automatic design of heat-resistant mutants, multiple computer-aided ΔΔGfold value calculation, and conservative analysis of mutation sites. AlyRm6A has naturally high thermostability. Compared with the WT, T43I and Q216I kept their original activities, and their half-lives were increased from 3.68 h to 4.29 h and 4.54 h, melting point temperatures increased from 61.5 °C to 62.9 °C and 63.5 °C, respectively. The results of circular dichroism showed that both the mutants and the wild type had the characteristic peaks of β-sheet at 195 nm and 216 nm, which indicated that there was no significant effect on the secondary structure of the protein. Molecular dynamics simulation (MD) analyses suggest that the enhancement of the hydrophobic interaction network, improvement of molecular rigidity, and denser structure could improve the stability of AlyRm6A. To the best of our knowledge, our findings indicate that AlyRm6A mutants exhibit the highest thermostability among the characterized PL-6 alginate lyases, making them potential candidates for industrial production of alginate oligosaccharides.

Mechanistic Elucidation of Activation/Deactivation Signal Transduction within Neurotensin Receptor 1 Triggered by 'Driver Chemical Groups' of Modulators: A Comparative Molecular Dynamics Simulation

Small-molecule modulators of neurotensin receptor 1 (NTSR1), a class A G-protein-coupled receptor (GPCR), has emerged as promising therapeutic agent for psychiatric disorders and cancer. Interestingly, a chemical group substitution in NTSR1 modulators can launch different types of downstream regulation, highlighting the significance of deciphering the internal fine-tuning mechanism. Here, we conducted a synergistic application of a Gaussian accelerated molecular dynamics simulation, a conventional molecular dynamics simulation, and Markov state models (MSM) to investigate the underlying mechanism of 'driver chemical groups' of modulators triggering inverse signaling. The results indicated that the flexibility of the leucine moiety in NTSR1 agonists contributes to the inward displacement of TM7 through a loosely coupled allosteric pathway, while the rigidity of the adamantane moiety in NTSR1 antagonists leads to unfavorable downward transduction of agonistic signaling. Furthermore, we found that R322^6.54, Y319^6.51, F353^7.42, R148^3.32, S356^7.45, and S357^7.46 may play a key role in inducing the activation of NTSR1. Together, our findings not only highlight the ingenious signal transduction within class A GPCRs but also lay a foundation for the development of targeted drugs harboring different regulatory functions of NTSR1.

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for disulfiram in cancer therapy

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.

An in silico approach to determine inter-subunit affinities in human septin complexes

The septins are a conserved family of filament-forming guanine nucleotide binding proteins, often named the fourth component of the cytoskeleton. Correctly assembled septin structures are required for essential intracellular processes such as cytokinesis, vesicular transport, polarity establishment, and cellular adhesion. Structurally, septins belong to the P-Loop NTPases but they do not mediate signals to effectors through GTP binding and hydrolysis. GTP binding and hydrolysis are believed to contribute to septin complex integrity, but biochemical approaches addressing this topic are hampered by the stability of septin complexes after recombinant expression and the lack of nucleotide-depleted complexes. To overcome this limitation, we used a molecular dynamics-based approach to determine inter-subunit binding free energies in available human septin dimer structures and in their apo forms, which we generated in silico. The nucleotide in the GTPase active subunits SEPT2 and SEPT7, but not in SEPT6, was identified as a stabilizing element in the G interface. Removal of GDP from SEPT2 and SEPT7 results in flipping of a conserved Arg residue and disruption of an extensive hydrogen bond network in the septin unique element, concomitant with a decreased inter-subunit affinity. Based on these findings we propose a singular "lock-hydrolysis" mechanism stabilizing human septin filaments.

Investigation of Molecular Interactions Mechanism of Pembrolizumab and PD-1

Human programmed cell death protein 1 (PD-1) is a checkpoint protein involved in the regulation of immune response. Antibodies are widely used as inhibitors that block the immune checkpoint, preventing strong immune responses. Pembrolizumab is an FDA-approved IgG4 antibody with PD-1 inhibitory ability for the treatment of melanoma. In this study, we investigated the effect of Pembrolizumab on the conformational changes in PD-1 using extensive molecular modeling and simulation approaches. Our study revealed that during the 200 ns simulation, the average values of the solvent accessible surface area, the radius of gyration, and internal hydrogen bonds of PD-1 were 64.46 nm², 1.38 nm and 78, respectively, while these values of PD-1 in the PD-1/Pembrolizumab complex were 67.29 nm², 1.39 nm and 76, respectively. The RMSD value of PD-1 gradually increased until 80 ns and maintained its stable conformation at 0.32 nm after 80 ns, while this value of PD-1 in the PD-1/Pembrolizumab complex maintained an increasing trend during 200 ns. The interaction between PD-1 and Pembrolizumab led to a flexible but stable structure of PD-1. PD-1 rotated around the rotation axis of the C'D loop and gradually approached Pembrolizumab. The number of hydrogen bonds involved in the interactions on the C and C' strands increased from 4 at 100 ns to 7 at 200 ns. The strong affinity of Pembrolizumab for the C'D and FG loops of PD-1 disrupted the interactions between PD-1 and PD-L1. Inhibition of the interaction between PD-1 and PD-L1 increased the T cell activity, and is effective in controlling and curing cancer. Further experimental work can be performed to support this finding.

Emerging Trends of Computational Chemistry and Molecular Modeling in Froth Flotation: A Review

Froth flotation is the most versatile process in mineral beneficiation, extensively used to concentrate a wide range of minerals. This process comprises mixtures of more or less liberated minerals, water, air, and various chemical reagents, involving a series of intermingled multiphase physical and chemical phenomena in the aqueous environment. Today's main challenge facing the froth flotation process is to gain atomic-level insights into the properties of its inherent phenomena governing the process performance. While it is often challenging to determine these phenomena via trial-and-error experimentations, molecular modeling approaches not only elicit a deeper understanding of froth flotation but can also assist experimental studies in saving time and budget. Thanks to the rapid development of computer science and advances in high-performance computing (HPC) infrastructures, theoretical/computational chemistry has now matured enough to successfully and gainfully apply to tackle the challenges of complex systems. In mineral processing, however, advanced applications of computational chemistry are increasingly gaining ground and demonstrating merit in addressing these challenges. Accordingly, this contribution aims to encourage mineral scientists, especially those interested in rational reagent design, to become familiarized with the necessary concepts of molecular modeling and to apply similar strategies when studying and tailoring properties at the molecular level. This review also strives to deliver the state-of-the-art integration and application of molecular modeling in froth flotation studies to assist either active researchers in this field to disclose new directions for future research or newcomers to the field to initiate innovative works.

Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

Artificial intelligence-based HDX (AI-HDX) prediction reveals fundamental characteristics to protein dynamics: Mechanisms on SARS-CoV-2 immune escape

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

A Comparison of Bonded and Nonbonded Zinc(II) Force Fields with NMR Data

Classical molecular dynamics (MD) simulations are widely used to inspect the behavior of zinc(II)-proteins at the atomic level, hence the need to properly model the zinc(II) ion and the interaction with its ligands. Different approaches have been developed to represent zinc(II) sites, with the bonded and nonbonded models being the most used. In the present work, we tested the well-known zinc AMBER force field (ZAFF) and a recently developed nonbonded force field (NBFF) to assess how accurately they reproduce the dynamic behavior of zinc(II)-proteins. For this, we selected as benchmark six zinc-fingers. This superfamily is extremely heterogenous in terms of architecture, binding mode, function, and reactivity. From repeated MD simulations, we computed the order parameter (S2) of all backbone N-H bond vectors in each system. These data were superimposed to heteronuclear Overhauser effect measurements taken by NMR spectroscopy. This provides a quantitative estimate of the accuracy of the FFs in reproducing protein dynamics, leveraging the information about the protein backbone mobility contained in the NMR data. The correlation between the MD-computed S2 and the experimental data indicated that both tested FFs reproduce well the dynamic behavior of zinc(II)-proteins, with comparable accuracy. Thus, along with ZAFF, NBFF represents a useful tool to simulate metalloproteins with the advantage of being extensible to diverse systems such as those bearing dinuclear metal sites.

Protein Design Using Physics Informed Neural Networks

The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.

Evolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of 'peptidase' domain of Spt16

Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses µs scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of ∼50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62% sequence identity with Sc-Spt16N) was solved at 1.7 Å resolution. Cumulative MD simulations of 6 µs were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N.Communicated by Ramaswamy H. Sarma.

Roadmap to Pyruvate Kinase M2 Modulation - A Computational Chronicle

Pyruvate kinase M2 (PKM2) has surfaced as a potential target for anti-cancer therapy. PKM2 is known to be overexpressed in the tumor cells and is a critical metabolic conduit in supplying the augmented bioenergetic demands of the recalcitrant cancer cells. The presence of PKM2 in structurally diverse tetrameric as well as dimeric forms has opened new avenues to design novel modulators. It is also a truism to state that drug discovery has advanced significantly from various computational techniques like molecular docking, virtual screening, molecular dynamics, and pharmacophore mapping. The present review focuses on the role of computational tools in exploring novel modulators of PKM2. The structural features of various isoforms of PKM2 have been discussed along with reported modulators. An extensive analysis of the structure-based and ligand- based in silico methods aimed at PKM2 modulation has been conducted with an in-depth review of the literature. The role of advanced tools like QSAR and quantum mechanics has been established with a brief discussion of future perspectives.

Predicting molecular properties of α-synuclein using force fields for intrinsically disordered proteins

Independent force field validation is an essential practice to keep track of developments and for performing meaningful Molecular Dynamics simulations. In this work, atomistic force fields for intrinsically disordered proteins (IDP) are tested by simulating the archetypical IDP α-synuclein in solution for 2.5 μs. Four combinations of protein and water force fields were tested: ff19SB/OPC, ff19SB/TIP4P-D, ff03CMAP/TIP4P-D, and a99SB-disp/TIP4P-disp, with four independent repeat simulations for each combination. We compare our simulations to the results of a 73 μs simulation using the a99SB-disp/TIP4P-disp combination, provided by D. E. Shaw Research. From the trajectories, we predict a range of experimental observations of α-synuclein and compare them to literature data. This includes protein radius of gyration and hydration, intramolecular distances, NMR chemical shifts, and ³ J-couplings. Both ff19SB/TIP4P-D and a99SB-disp/TIP4P-disp produce extended conformational ensembles of α-synuclein that agree well with experimental radius of gyration and intramolecular distances while a99SB-disp/TIP4P-disp reproduces a balanced α-synuclein secondary structure content. It was found that ff19SB/OPC and ff03CMAP/TIP4P-D produce overly compact conformational ensembles and show discrepancies in the secondary structure content compared to the experimental data.

A homozygous loss-of-function variant in the MPO gene is associated with generalized pustular psoriasis

Generalized pustular psoriasis (GPP) is a rare form of psoriasis, which is characterized by sudden onset of repeated erythema and pustule formation with generalized inflammation. Recent advances in molecular genetics have led to the identification of several genes associated with GPP, including IL36RN, CARD14, AP1S3, SERPINA3, and MPO. Of these, only limited cases of GPP have been reported to carry mutations in the AP1S3, SERPINA3, or MPO to date. In the present study, we investigated a Japanese patient with GPP and found a homozygous missense mutation c.1769G>T (p.Arg590Leu) in the MPO gene. Structural analysis predicted that the mutant MPO protein would abolish its ability to bind with heme protein. In vitro studies using cultured cells revealed that the mutant MPO was stably expressed, but completely lost its myeloperoxidase activity. Immunohistochemistry (IHC) using an anti-MPO antibody showed markedly reduced expression of MPO protein in the patient's skin, suggesting that the mutation would lead to an instability of the MPO protein in vivo. Finally, IHC with an anti-citrullinated Histone H3 antibody demonstrated a sparse formation of neutrophil extracellular traps within a Kogoj's spongiform pustule of the patient's skin. Collectively, we conclude that the c.1769G>T (p.Arg590Leu) in the MPO is a complete loss-of-function mutation associated with GPP in the patient. Our data further underscore critical roles of the MPO gene in the pathogenesis of GPP.

Signal transfer in human protein tyrosine phosphatase PTP1B from allosteric inhibitor P00058

Protein tyrosine phosphatases constitute a family of cytosolic and receptor-like signal transducing enzymes that catalyze the hydrolysis of phospho-tyrosine residues of phosphorylated proteins. PTP1B, encoded by PTPN1, is a key negative regulator of insulin and leptin receptor signaling, linking it to two widespread diseases: type 2 diabetes mellitus and obesity. Here, we present crystal structures of the PTP1B apo-enzyme and a complex with a newly identified allosteric inhibitor, 2-(2,5-dimethyl-pyrrol-1-yl)-5-hydroxy-benzoic acid, designated as P00058. The inhibitor binding site is located about 18 Å away from the active center. However, the inhibitor causes significant re-arrangements in the active center of enzyme: residues 45-50 of catalytic Tyr-loop are shifted at their Cα-atom positions by 2.6 to 5.8 Å. We have identified an event of allosteric signal transfer from the inhibitor to the catalytic area using molecular dynamic simulation. Analyzing change of complex structure along the fluctuation trajectory we have found the large Cα-atom shifts in external strand, residues 25-40, which occur at the same time with the shifts in adjacent catalytic p-Tyr-loop. Coming of the signal to this loop arises due to dynamic fluctuation of protein structure at about 4.0 nanoseconds after the inhibitor takes up its space. Communicated by Ramaswamy H. Sarma.

One microsecond MD simulations of the SARS-CoV-2 main protease and hydroxychloroquine complex reveal the intricate nature of binding

Currently, several vaccines and antivirals across the globe are in clinical trials. Hydroxychloroquine (HCQ) was reported to inhibit the SARS-CoV-2 virus in antiviral assays. Here, it raises the curiosity about the molecular target of HCQ inside the cell. It may inhibit some of the viral targets, or some other complex mechanisms must be at disposal towards action mechanisms. In some of the viruses, proteases are experimentally reported to be a potential target of HCQ. However, no in-depth investigations are available in the literature yet. Henceforth, we have carried out extensive, one-microsecond long molecular dynamics simulations of the bound complex of hydroxychloroquine with main protease (Mpro) of SARS-CoV-2. Our analysis found that HCQ binds within the catalytic pocket of Mpro and remains stable upto one-third of simulation time but further causes increased fluctuations in simulation parameters. In the end, the HCQ does not possess any pre-formed hydrogen bond, other non-covalent interactions with Mpro, ultimately showing the unsteadiness in binding at catalytic binding pocket and may suggest that HCQ may not inhibit the Mpro. In the future, this study would require experimental validation on enzyme assays against Mpro, and that may be the final say. Communicated by Ramaswamy H. Sarma.

Binding and Functional Folding (BFF): A Physiological Framework for Studying Biomolecular Interactions and Allostery

EF-hand Ca2+-binding proteins (CBPs), such as S100 proteins (S100s) and calmodulin (CaM), are signaling proteins that undergo conformational changes upon increasing intracellular Ca2+. Upon binding Ca2+, S100 proteins and CaM interact with protein targets and induce important biological responses. The Ca2+-binding affinity of CaM and most S100s in the absence of target is weak (CaKD > 1 μM). However, upon effector protein binding, the Ca2+ affinity of these proteins increases via heterotropic allostery (CaKD < 1 μM). Because of the high number and micromolar concentrations of EF-hand CBPs in a cell, at any given time, allostery is required physiologically, allowing for (i) proper Ca2+ homeostasis and (ii) strict maintenance of Ca2+-signaling within a narrow dynamic range of free Ca2+ ion concentrations, [Ca2+]free. In this review, mechanisms of allostery are coalesced into an empirical "binding and functional folding (BFF)" physiological framework. At the molecular level, folding (F), binding and folding (BF), and BFF events include all atoms in the biomolecular complex under study. The BFF framework is introduced with two straightforward BFF types for proteins (type 1, concerted; type 2, stepwise) and considers how homologous and nonhomologous amino acid residues of CBPs and their effector protein(s) evolved to provide allosteric tightening of Ca2+ and simultaneously determine how specific and relatively promiscuous CBP-target complexes form as both are needed for proper cellular function.

Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels

The enzyme enoyl-ACP reductase (FabI) is the limiting step of the membrane's fatty acid biosynthesis in bacteria and a druggable target for novel antibacterial agents. The FabI active form is a homotetramer, which displays the highest affinity to inhibitors. Herein, molecular dynamics studies were carried out using the structure of FabI in complex with known inhibitors to investigate their effects on tetramerization. Our results suggest that multimerization is essential for the integrity of the catalytic site and that inhibitor binding enables the multimerization by stabilizing the substrate binding loop (SBL, L:195-200) coupled with changes in the H4/5 (QR interface). We also observed that AFN-1252 (naphtpyridinone derivative) promotes unique conformational changes affecting monomer-monomer interfaces. These changes are induced by AFN-1252 interaction with key residues in the binding sites (Ala95, Tyr146, and Tyr156). In addition, the analysis of water trajectories indicated that AFN-1252 complexes allow more water molecules to enter the binding site than triclosan and MUT056399 complexes. FabI-AFN-1252 simulations show accumulation of water molecules near the Tyr146/147 pocket, which can become a hotspot to the design of novel FabI inhibitors.

Structure of SARS-CoV-2 M protein in lipid nanodiscs

SARS-CoV-2 encodes four structural proteins incorporated into virions, spike (S), envelope (E), nucleocapsid (N), and membrane (M). M plays an essential role in viral assembly by organizing other structural proteins through physical interactions and directing them to sites of viral budding. As the most abundant protein in the viral envelope and a target of patient antibodies, M is a compelling target for vaccines and therapeutics. Still, the structure of M and molecular basis for its role in virion formation are unknown. Here, we present the cryo-EM structure of SARS-CoV-2 M in lipid nanodiscs to 3.5 Å resolution. M forms a 50 kDa homodimer that is structurally related to the SARS-CoV-2 ORF3a viroporin, suggesting a shared ancestral origin. Structural comparisons reveal how intersubunit gaps create a small, enclosed pocket in M and large open cavity in ORF3a, consistent with a structural role and ion channel activity, respectively. M displays a strikingly electropositive cytosolic surface that may be important for interactions with N, S, and viral RNA. Molecular dynamics simulations show a high degree of structural rigidity in a simple lipid bilayer and support a role for M homodimers in scaffolding viral assembly. Together, these results provide insight into roles for M in coronavirus assembly and structure.

Interacting binding insights and conformational consequences of the differential activity of cannabidiol with two endocannabinoid-activated G-protein-coupled receptors

Cannabidiol (CBD), the major non-psychoactive phytocannabinoid present in the plant Cannabis sativa, has displayed beneficial pharmacological effects in the treatment of several neurological disorders including, epilepsy, Parkinson’s disease, and Alzheimer’s disease. In particular, CBD is able to modulate different receptors in the endocannabinoid system, some of which belong to the family of G-protein-coupled receptors (GPCRs). Notably, while CBD is able to antagonize some GPCRs in the endocannabinoid system, it also seems to activate others. The details of this dual contrasting functional feature of CBD, that is, displaying antagonistic and (possible) agonistic ligand properties in related receptors, remain unknown. Here, using computational methods, we investigate the interacting determinants of CBD in two closely related endocannabinoid-activated GPCRs, the G-protein-coupled receptor 55 (GPR55) and the cannabinoid type 1 receptor (CB₁). While in the former, CBD has been demonstrated to function as an antagonist, the way by which CBD modulates the CB₁ receptor remains unclear. Namely, CBD has been suggested to directly trigger receptor’s activation, stabilize CB₁ inactive conformations or function as an allosteric modulator. From microsecond-length unbiased molecular dynamics simulations, we found that the presence of the CBD ligand in the GPR55 receptor elicit conformational changes associated with antagonist-bound GPCRs. In contrast, when the GPR55 receptor is simulated in complex with the selective agonist ML186, agonist-like conformations are sampled. These results are in agreement with the proposed modulatory function of each ligand, showing that the computational techniques utilized to characterize the GPR55 complexes correctly differentiate the agonist-bound and antagonist-bound systems. Prompted by these results, we investigated the role of the CBD compound on the CB₁ receptor using similar computational approaches. The all-atom MD simulations reveal that CBD induces conformational changes linked with agonist-bound GPCRs. To contextualize the results we looked into the CB₁ receptor in complex with a well-established antagonist. In contrast to the CBD/CB₁ complex, when the CB₁ receptor is simulated in complex with the ligand antagonist AM251, inactive conformations are explored, showing that the computational techniques utilized to characterize the CB₁ complexes correctly differentiate the agonist-bound and antagonist-bound systems. In addition, our results suggest a previously unknown sodium-binding site located in the extracellular domain of the CB₁ receptor. From our detailed characterization, we found particular interacting loci in the binding sites of the GPR55 and the CB₁ receptors that seem to be responsible for the differential functional features of CBD. Our work will pave the way for understanding the CBD pharmacology at a molecular level and aid in harnessing its potential therapeutic use.

Specific anion effect on properties of HRV 3C protease

Specific salts effect is intensively studied from the prospective of modification of different physico-chemical properties of biomacromolecules. Limited knowledge of the specific salts effect on enzymes led us to address the influence of five sodium anions: sulfate, phosphate, chloride, bromide, and perchlorate, on catalytic and conformational properties of human rhinovirus-14 (HRV) 3C protease. The enzyme conformation was monitored by circular dichroism spectrum (CD) and by tyrosines fluorescence. Stability and flexibility of the enzyme have been analyzed by CD in the far-UV region, differential scanning calorimetry and molecular dynamics simulations, respectively. We showed significant influence of the anions on the enzyme properties in accordance with the Hofmeister effect. The HRV 3C protease in the presence of kosmotropic anions, in contrast with chaotropic anions, exhibits increased stability, rigidity. Correlations of stabilization effect of anions on the enzyme with their charge density and the rate constant of the enzyme with the viscosity B-coefficients of anions suggest direct interaction of the anions with HRV 3C protease. The role of stabilization and decreased fluctuation of the polypeptide chain of HRV 3C protease on its activation in the presence of kosmotropic anions is discussed within the frame of the macromolecular rate theory.