General Computational Strategy Research Articles

A general computational design strategy for stabilizing viral class I fusion proteins

Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Using general computational chemistry strategy to unravel the reactivity of emerging pollutants: An example of sulfonamide chlorination

Increasing number of emerging pollutants in environments requires an effective approach which can facilitate the prediction of reactivity and provide insights into the reaction mechanisms. Computational chemistry is exactly the tool to fulfill this demand with its good performance in theoretical investigation of chemical reactions at molecular level. In this study, chlorination of sulfonamide antibiotics is used as an illustration to present a systematic strategy demonstrating how computational chemistry can be applied to investigate the reaction behavior of emerging pollutants. Sulfonamides is a class of micropollutants that contain the common structure of 4-aminobenzenesulfonmaide while differ in their heterocycles. Based on the calculated conceptual DFT indices, the reactive sites of sulfonamide are successfully predicted, which locate on their common structure of 4-aminobenzenesulfonmaide. Therefore, all sulfonamides follow the similar reaction pathway. Product identification by LTQ-Orbitrap MS further verifies the in silico prediction. Three critical pathways are discovered, i.e., S-N bond cleavage, Cl-substitution onto aniline-N, and the following rearrangement to lose -SO2- group, among which Cl-substitution is the key step due to its lowest free energy barrier. Heterocycles impact the reaction rate by affecting the electronic density of aniline group. In general, the more electron-donating the heterocycle is, the more readily sulfonamides to be chlorinated.

On the Modelling of Enterprise Level Crop Yields

A model for the approximation of enterprise-level crop yields under climate change is proposed which integrates economic and agronomic notions of crop production. The model is potentially useful to policy makers, since it could provide an estimate of the yield volume that can be expected to be produced under climate change; it could increase prediction accuracy compared to farm-level programming models by including omitted factors, e.g. wage. This framework can also be useful to farmers as a normative model recommending input commitment under a changing climate. A general computational strategy for the model is provided. A geometric definition of the coefficient of risk-aversion, which is intrinsic to the farmer, is offered. An approach to infer the degree of risk-aversion from farm-level accounting data is proposed. The identification strategy for the coefficient of risk-aversion relies on using the duality between the possible production technology representations, suitable data separation and the employment of a grid-search. The introduced definition of the coefficient of risk aversion and the strategy to its identification also hold in the deterministic case for intertemporal preferences.

Computational design and optimization of novel d-peptide TNFα inhibitors.

Compared to small molecule drugs, peptide therapeutics provides greater efficacy, selectivity, and safety. The intrinsic disadvantages of peptides are their sensitivity to proteases. To overcome this, we have developed a general computational strategy for denovo design of protein binding helical d-peptides. A d-helical fragment library was established and used in generating flexible d-helical conformations, which were then used to generate suitable sequences with the required structural and binding properties. Using this strategy, we successfully denovo designed d-helical peptides that bind to tumor necrosis factor-α (TNFα), inhibit TNFα-TNFR1 binding, reduce TNFα activity in cellular assays, and are stable against protease digestion. Our strategy of helical d-peptide design is generally applicable for discovering d-peptide modulators against protein-protein interactions.

Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation

The simulation of open quantum dynamics is a critical tool for understanding how the non-classical properties of matter might be functionalised in future devices. However, unlocking the enormous potential of molecular quantum processes is highly challenging due to the very strong and non-Markovian coupling of ‘environmental’ molecular vibrations to the electronic ‘system’ degrees of freedom. Here, we present an advanced but general computational strategy that allows tensor network methods to effectively compute the non-perturbative, real-time dynamics of exponentially large vibronic wave functions of real molecules. We demonstrate how ab initio modelling, machine learning and entanglement analysis can enable simulations which provide real-time insight and direct visualisation of dissipative photophysics, and illustrate this with an example based on the ultrafast process known as singlet fission.

Protocol for Computational Enzymatic Reactivity Based on Geometry Optimisation.

Enzymes play a biologically essential role in performing and controlling an important share of the chemical processes occurring in life. However, despite their critical role in nature, attaining a clear understanding of the way an enzyme acts is still cumbersome. Computational enzymology is playing an increasingly important role in this field of research. It allows the elucidation of a complete and detailed mechanism of an enzymatic reaction, including the characterization of reaction intermediates and transition states from both structural and energetic points of view, which is something that no other single experiment can produce alone. In this review, we present a general computational strategy to study enzymatic mechanisms based on adiabatic mapping and free geometry optimization. These methods allow chemical reactions to be studied with high theoretical levels, and allow a more exhaustive exploration of the chemical reactional space than other available methods, albeit being limited to the extent that they explore the enzyme conformational space. Special attention is given to the choice of the theoretical levels, as well as describing the model systems that are currently used to study enzymatic reactions. With this, we aim to provide a good introduction for non-specialised users in this field of research. We also provide a selection of hand-picked examples from our own work that illustrate the power of computational enzymology to study catalytic mechanisms. Some of these studies constitute pioneering work in the field that were later validated by experimental means.

Surface defects and instanton partition functions

We study the superconformal index of five-dimensional SCFTs and the sphere partition function of four-dimensional gauge theories with eight supercharges in the presence of co-dimension two half-BPS defects. We derive a prescription which is valid for defects which can be given a "vortex construction", i.e. can be defined by RG flow from vortex configurations in a larger theory. We test the prescription against known results and expected dualities. We employ our prescription to develop a general computational strategy for defects defined by coupling the bulk degrees of freedom to a Gauged Linear Sigma Model living in co-dimension two.

A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons.

Sensory input reflects events that occur in the environment, but multiple events may be confounded in sensory signals. For example, under many natural viewing conditions, retinal image motion reflects some combination of self-motion and movement of objects in the world. To estimate one stimulus event and ignore others, the brain can perform marginalization operations, but the neural bases of these operations are poorly understood. Using computational modeling, we examine how multisensory signals may be processed to estimate the direction of self-motion (i.e., heading) and to marginalize out effects of object motion. Multisensory neurons represent heading based on both visual and vestibular inputs and come in two basic types: "congruent" and "opposite" cells. Congruent cells have matched heading tuning for visual and vestibular cues and have been linked to perceptual benefits of cue integration during heading discrimination. Opposite cells have mismatched visual and vestibular heading preferences and are ill-suited for cue integration. We show that decoding a mixed population of congruent and opposite cells substantially reduces errors in heading estimation caused by object motion. In addition, we present a general formulation of an optimal linear decoding scheme that approximates marginalization and can be implemented biologically by simple reinforcement learning mechanisms. We also show that neural response correlations induced by task-irrelevant variables may greatly exceed intrinsic noise correlations. Overall, our findings suggest a general computational strategy by which neurons with mismatched tuning for two different sensory cues may be decoded to perform marginalization operations that dissociate possible causes of sensory inputs.

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network.

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a "megamap," or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world.

Molecular clock calibrations and substitution rates as a theoretical framework for systems approaches in TLRs: A perspective for drug targeting in leishmaniasis

The Toll like receptors (TLRs) represents a set of evolutionary conserved proteins that are responsible for the recognition of the molecular patterns of several pathogens like Leishmania. Present on the mammalian macrophages, they initiate an appropriate inflammatory response against the pathogen. Recognition of the pathogen triggers a complex signaling cascade which is required to sustain an appropriate immune response. TLRs therefore hold promise in the drug discovery research as suitable drug targets. The present study is focused on a system's based phylogenetic analysis of the TLRs which focuses primarily on the importance of TLR1–9 in the infectious disease ‘Leishmaniasis’. Our phylogenetic analysis has shown that TLR6 family is evolutionary conserved among all the host TLRs. Developing a lipopeptide analog as a TLR6 ligand would thus act as an agonist to the TLR6 signaling. In general, the ability to target TLR6 by an analysis of the pattern of conservation and divergence served as a general computational strategy to identify drug targets for targeting infectious diseases.

Molecular Dynamics Studies of Proton Transport in Hydrogenase and Hydrogenase Mimics.

There is extensive interest in hydrogenases based on their ability to rapidly and efficiently interconvert H2 with protons and electrons, and their (typically) superior function relative to molecular mimics. Understanding the function of enzymes is one approach to implementing design features to make better catalysts and is an approach we have implemented in our work. In this review, we will discuss our efforts to develop design principles from enzymes, with specific focus on proton transport. We will also present computational studies of the mimics we have investigated with similar methodologies. We will discuss the mechanisms used by small scaffolds on molecular mimics which in many cases are surprisingly similar to those used by nature, while in other cases, computational analysis allowed us to reveal an unexpected role. Computational methods provide one of the best ways, and in some cases, the only way, to gain insight into the mechanistic details of enzymes. In this review, we illustrate the general computational strategy we used to study the proton pathway of [FeFe]-hydrogenase, and the similar strategy to investigate small molecules. We present the main results we obtained and how our computational work stimulated or worked in concert with experimental investigations. We also focus on estimation of errors and convergence of properties in the simulations. These studies demonstrate the powerful results that can be obtained by the close pairing of experimental and theoretical approaches.

Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation.

How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Dynamics of the Photogenerated Hole at the Rutile TiO2(110)/Water Interface: A Nonadiabatic Simulation Study

Hydrogen production in photoelectrochemical cells constitutes an important avenue toward carbon-free fuel. The most convenient process for hydrogen production is the splitting of water molecules, which necessitates a catalytic reaction involving a semiconductor. Here, we introduce a framework for the study of photocatalyzed reactions on semiconductor surfaces based on time-dependent density functional theory that explicitly accounts for the evolution of electronically excited states. Within this framework, we investigate the possibility of hole-mediated splitting of molecularly adsorbed water on a representative metal oxide surface—the rutile TiO2(110). We find that oxidative dehydrogenation of water is possible in synergy with thermal effects at temperatures between 60 and 100 K only when defects like Ti interstitials are present in the subsurface region. This study presents a general computational strategy for describing photoexcited semiconductor/adsorbate interfaces and also demonstrates that the occurrence of water dissociation on the rutile TiO2(110) surface depends sensitively on the local atomic environment and external parameters such as temperature.

Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach

A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the proteins were located in the cytoplasm, out of which 6% was virulent. Finally, this framework may serve as a general computational strategy for future drug target identification in infectious diseases.

Colocalization of Coregulated Genes: A Steered Molecular Dynamics Study of Human Chromosome 19

The connection between chromatin nuclear organization and gene activity is vividly illustrated by the observation that transcriptional coregulation of certain genes appears to be directly influenced by their spatial proximity. This fact poses the more general question of whether it is at all feasible that the numerous genes that are coregulated on a given chromosome, especially those at large genomic distances, might become proximate inside the nucleus. This problem is studied here using steered molecular dynamics simulations in order to enforce the colocalization of thousands of knowledge-based gene sequences on a model for the gene-rich human chromosome 19. Remarkably, it is found that most () gene pairs can be brought simultaneously into contact. This is made possible by the low degree of intra-chromosome entanglement and the large number of cliques in the gene coregulatory network. A clique is a set of genes coregulated all together as a group. The constrained conformations for the model chromosome 19 are further shown to be organized in spatial macrodomains that are similar to those inferred from recent HiC measurements. The findings indicate that gene coregulation and colocalization are largely compatible and that this relationship can be exploited to draft the overall spatial organization of the chromosome in vivo. The more general validity and implications of these findings could be investigated by applying to other eukaryotic chromosomes the general and transferable computational strategy introduced here.

Genotype imputation with thousands of genomes.

Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.

Reaction Pathway and Free Energy Profile for Prechemical Reaction Step of Human Butyrylcholinesterase-Catalyzed Hydrolysis of (−)-Cocaine by Combined Targeted Molecular Dynamics and Potential of Mean Force Simulations

Combined targeted molecular dynamics (TMD) and potential of mean force (PMF) simulations have been carried out to uncover the detailed pathway and determine the corresponding free energy profile for the structural transformation from the nonprereactive butyrylcholinesterase (BChE)-(-)-cocaine binding to the prereactive BChE-(-)-cocaine binding associated with the (-)-cocaine rotation in the binding pocket of BChE. It has been shown that the structural transformation involves two transition states (TS1(rot) and TS2(rot)). TS1(rot) is mainly associated with the deformation of the nonprereactive complex, whereas TS2(rot) is mainly associated with the formation of the prereactive complex. It has also been demonstrated that the A328W/Y332G mutation significantly reduces the steric hindrance for (-)-cocaine rotation in the binding pocket of BChE and, thus, decreases the free energy barrier for the structural transformation from the nonprereactive binding to the prereactive binding. The calculated relative free energy barriers are all consistent with available experimental kinetic data. The new mechanistic insights obtained and the novel computational protocol tested in this study should be valuable for future computational design of high-activity mutants of BChE. The general computational strategy and approach based on the combined TMD and PMF simulations may be also valuable in computational studies of detailed pathways and free energy profiles for other similar mechanistic problems involving ligand rotation or another type of structural transformation in the binding pocket of a protein.

Free Energy Perturbation (FEP) Simulation on the Transition States of Cocaine Hydrolysis Catalyzed by Human Butyrylcholinesterase and Its Mutants

A novel computational protocol based on free energy perturbation (FEP) simulations on both the free enzyme and transition state structures has been developed and tested to predict the mutation-caused shift of the free energy change from the free enzyme to the rate-determining transition state for human butyrylcholinesterase (BChE)-catalyzed hydrolysis of (-)-cocaine. The calculated shift, denoted by DeltaDeltaG(1 --> 2), of such kind of free energy change determines the catalytic efficiency (kcat/KM) change caused by the simulated mutation transforming enzyme 1 to enzyme 2. By using the FEP-based computational protocol, the DeltaDeltaG(1 --> 2) values for the mutations A328W/Y332A --> A328W/Y332G and A328W/Y332G --> A328W/Y332G/A199S were calculated to be -0.22 and -1.94 kcal/mol, respectively. The calculated DeltaDeltaG(1 --> 2) values predict that the change from the A328W/Y332A mutant to the A328W/Y332G mutant should slightly improve the catalytic efficiency and that the change from the A328W/Y332G mutant to the A328W/Y332G/A199S mutant should significantly improve the catalytic efficiency of the enzyme for the (-)-cocaine hydrolysis. The predicted catalytic efficiency increases are supported by the experimental data showing that kcat/KM = 8.5 x 10(6), 1.4 x 10(7), and 7.2 x 10(7) min(-1) M(-1) for the A328W/Y332A, A328W/Y332G, and A328W/Y332G/A199S mutants, respectively. The qualitative agreement between the computational and experimental data suggests that the FEP simulations may provide a promising protocol for rational design of high-activity mutants of an enzyme. The general computational strategy of the FEP simulation on a transition state can be used to study the effects of a mutation on the activation free energy for any enzymatic reaction.

Periodic and High-Temperature Disordered Conformations of Polytetrafluoroethylene Chains: An ab Initio Modeling

We report here the main results of a successful attempt to predict some macroscopic properties of representative polymers of technological relevance both in regular and disordered forms by using first principle quantum mechanical approaches at microscopic level. Until now, the prediction of the structural and thermal properties of those polymers has been mostly a domain of molecular mechanics methods. To overcome the limits of those classical computational tools whenever physical properties are significantly influenced by stereoelectronic effects (e.g., electron rich substituents), we employed methods rooted in the Density Functional Theory (DFT). A general computational strategy including the proper choice of periodic boundary conditions (PBC), functional, basis set, and model system size, has been tested and validated for saturated polymers such as polyethylene and isotactic/syndiotactic polypropylenes. On the basis of these results, a comprehensive study of poly(tetrafluoroethylene) (PTFE) chains in both regular periodic and disordered conformations has been performed. A statistical approach has been next applied to obtain the thermal concentration of defects and to reproduce the thermal behavior of the investigated polymer. At the end, a very good agreement with experimental X-ray diffraction and IR results has been achieved, definitely reaching a good understanding of the widely studied disorder phenomenon determining the main technological properties of poly(tetrafluoroethylene) (the trade Teflon) and, at the same time, identifying the proper computational tools to investigate perfluoro-compounds and other complex polymeric systems.

Design of a novel globular protein fold with atomic-level accuracy.

A major challenge of computational protein design is the creation of novel proteins with arbitrarily chosen three-dimensional structures. Here, we used a general computational strategy that iterates between sequence design and structure prediction to design a 93-residue alpha/beta protein called Top7 with a novel sequence and topology. Top7 was found experimentally to be folded and extremely stable, and the x-ray crystal structure of Top7 is similar (root mean square deviation equals 1.2 angstroms) to the design model. The ability to design a new protein fold makes possible the exploration of the large regions of the protein universe not yet observed in nature.