Implementation of residue-level coarse-grained models in GENESIS for large-scale molecular dynamics simulations

Alpha lipoic acid modulates metabolic reprogramming in breast cancer stem cells enriched 3D spheroids by targeting phosphoinositide 3-kinase: In silico and in vitro insights

Breast cancer stem cells (BCSCs) are a unique subpopulation of tumor cells driving tumor resistance, progression, metastasis, and recurrence. Reprogrammed cellular metabolism and key signaling pathways, including Wnt/β-catenin, TGF-β, STAT3, and PI3K/AKT/mTOR pathway play a vital role in maintaining BCSCs. Importantly, PI3K/Akt/mTOR pathway regulates metabolism, survival, growth, and invasion, with PIK3CA, encoding the PI3K catalytic subunit p110α, the most frequently mutated gene in breast cancer. This study investigates the effects of alpha-lipoic acid (LA) on the metabolic profile of BCSCs, focusing on its interaction with PI3K signaling. LA was found to bind PI3K, disrupting cancer-associated metabolic pathways and significantly inhibiting BCSC metabolism. Metabolomic analysis of MCF-7 and MDA-MB-231-derived breast cancer spheroids showed LA-induced metabolic shifts. In MCF-7 spheroids, LA induced upaccumulation of 15 metabolites and downaccumulation of 5, while in MDA-MB-231 spheroids, it induced upaccumulation of 3 and downaccumulation of 16. LA also enhanced the sensitivity of breast cancer spheroids to doxorubicin (Dox), demonstrating a synergistic effect. Mechanistically, LA modulates the PI3K/Akt/mTOR pathway, impairing cell survival and proliferation. These findings highlight the potential of LA as a therapeutic agent for reprogramming cancer metabolism and enhancing chemotherapy efficacy. These results provide a strong rationale for incorporating LA into combination therapy strategies for breast cancer treatment.

Computer-directed rational engineering of dioxygenase TcsAB for triclosan biodegradation under cold conditions

The dioxygenase TcsAB is a specific dioxygenase involved in the initial biodegradation of the broad-spectrum antibacterial agent triclosan (TCS). However, it exhibits significantly reduced activity under cold conditions. In this study, a computer-directed approach combining loop engineering and N-terminal truncation was utilized to decrease the thermostability of TcsAB, thereby enhancing its catalytic activity in cold environments. The iterative mutant TcsAB (TcsAY277P/F279P/S311W/A313WTcsBN-terminal truncation) exhibited a 2.54-fold greater catalytic efficiency than the wild type at 15°C. Molecular dynamics simulations showed that the mutations introduced in the substrate-binding pocket increased its flexibility, leading to enhanced catalytic activity through binding in a more advantageous conformation. This modified dioxygenase was employed as a biological component, and Pseudomonas knackmussii B13 was used as a chassis cell to construct an engineered strain for the efficient degradation of TCS at low temperatures. The objective was to enhance the capacity of TCS bioremediation in natural environments. Insights gained from this study may inform the rational redesign of enzymes related to the robustness of biodegradation of emerging contaminants.IMPORTANCEThe presence of TCS in surface water and wastewater poses a significant risk to aquatic organisms and human health due to its high resistance to degradation. The biodegradation of TCS pollution in the environment through the metabolic processes of microorganisms represents a significant and effective remediation strategy. The dioxygenase TcsAB is the only specific enzyme that has been identified as responsible for the initial biodegradation of TCS. Nevertheless, the enzyme activity responsible for the degradation of TCS was markedly diminished at low temperatures. The actual ambient temperature is frequently lower than the optimum temperature for enzyme reaction, and maintaining the 30°C reaction condition results in high costs and energy consumption for TCS removal. Accordingly, the rational engineering of dioxygenase TcsAB for low-temperature activity will facilitate more efficient and realistic removal of TCS in an aqueous environment.

A comprehensive pharmacology study reveals the molecular mechanisms underlying the antidepressant effects of Gastrodiae Rhizoma

BACKGROUND

Gastrodiae Rhizoma (GR) and its extract have been widely used in the treatment of depression, but the underlying mechanism of its antidepressant effects is unclear due to its numerous components.

PURPOSE

Revealing the cellular and molecular mechanisms underlying the antidepressant effects of GR through a comprehensive pharmacology-based in vivo and in vitro investigation.

METHODS

A mouse model of depression was established using chronic mild stress (CMS) procedure, and the antidepressant effects of GR were evaluated using systematic behavior. Metabolites in GR decoction and in mouse brain were identified by UPLC-QTOF-MS technology. Core components and targets of GR against MDD were screened based on network pharmacology analysis and molecular docking. The mechanism through which GR mitigated MDD was explored using transcriptome analysis, immunohistochemistry and western blotting in vitro and in vivo.

RESULTS

A total of 273 components were identified in the GR decoction, out of which 15 were detected in the brain of depressed mice treated with the GR decoction. We further identified nine key active ingredients, six essential targets, and fifth signaling pathways associated with the therapeutic effects of GR against MDD. We confirmed that the active ingredients of GR can target the neural stem/precursor cells (NSPCs) in the hippocampus of depressed mice to promote neurogenesis, as evidenced by a significant increase in the numbers of DCX+ cells, BrdU+ cells, BrdU+-DCX+ cells, and BrdU+-NeuN+ cells within the hippocampus of GR-treated mice compared to salinetreated mice under CMS exposure. Moreover, we have identified that the key active constituents of GR, namely gastrodin and parishin C, exert a targeted effect on EGFR to activate PI3K-Akt signaling in NSPCs, thereby facilitating proliferation and differentiation of NSPCs.

CONCLUSION

The antidepressant effect of GR involves the facilitation of PI3K/Akt-mediated neurogenesis through gastrodin and parishin C targeting EGFR in NSPCs.

Heterogeneous condensates of transcription factors in embryonic stem cells: Molecular simulations

Biomolecular condensates formed via liquid-liquid phase separation are ubiquitous in cells, especially in the nucleus. While condensates containing one or two kinds of biomolecules have been relatively well characterized, those with more heterogeneous biomolecular components and interactions between biomolecules inside are largely unknown. This study used residue-resolution molecular dynamics simulations to investigate heterogeneous protein assemblies that include four master transcription factors in mammalian embryonic stem cells: Oct4, Sox2, Klf4, and Nanog. Molecular dynamics simulations of the mixture systems showed highly heterogeneous and dynamic behaviors; protein condensates mainly contain Sox2, Klf4, and Nanog, while most Oct4 are dissolved into the dilute phase. The condensate forms loosely interacting clusters where Klf4 is the most abundant, suggesting that Klf4 serves as a scaffold of the condensate, and Sox2 and Nanog are bound to Klf4 for stabilizing the condensate. Oct4 is moderately recruited to the condensate, serving as a client mainly via its interaction with Sox2. This study highlights the importance of intermolecular interaction between different transcription factors on the condensate formations with heterogeneous biomolecular components.

In silico nanoscope to study the interplay of genome organization and transcription regulation

In eukaryotic genomes, regulated access and communication between cis-regulatory elements (CREs) are necessary for enhancer-mediated transcription of genes. The molecular framework of the chromatin organization underlying such communication remains poorly understood. To better understand it, we develop a multiscale modeling pipeline to build near-atomistic models of the 200 kb Nanog gene locus in mouse embryonic stem cells comprising nucleosomes, transcription factors, co-activators, and RNA polymerase II-mediator complexes. By integrating diverse experimental data, including protein localization, genomic interaction frequencies, cryo-electron microscopy, and single-molecule fluorescence studies, our model offers novel insights into chromatin organization and its role in enhancer-promoter communication. The models equilibrated by high-performance molecular dynamics simulations span a scale of ∼350 nm, revealing an experimentally consistent local and global organization of chromatin and transcriptional machinery. Our models elucidate that the sequence-regulated chromatin accessibility facilitates the recruitment of transcription regulatory proteins exclusively at CREs, guided by the contrasting nucleosome organization compared to other regions. By constructing an experimentally consistent near-atomic model of chromatin in the cellular environment, our approach provides a robust framework for future studies on nuclear compartmentalization, chromatin organization, and transcription regulation.

An integrative approach for mechanistic insights into the atherosclerotic plaque-stabilizing properties of Danggui Buxue decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Danggui Buxue Decoction (DBD), a traditional Chinese medicinal formula, has historically been used for cardiovascular health, including managing atherosclerotic plaques (ASP). However, its precise mechanisms remain elusive.

AIM OF THE STUDY

The purpose of this study was to use a novel integrative bioinformatics analysis and experimental validation approach to provide a molecular basis for ASP's stabilization by DBD.

MATERIALS AND METHODS

A mice model of ApoE-deficient atherosclerosis fed with a high-fat diet was employed to evaluate the efficacy of DBD in stabilizing ASP. The potential mechanism underlying the stabilization effect of DBD on ASP was systematically investigated using an integrated approach combining network pharmacology, molecular docking, and molecular dynamics simulation. Additionally, an ox-LDL-induced macrophage foam cell model and multivariate statistical analysis were utilized to validate the pharmacodynamic material basis and target of DBD in stabilizing ASP.

RESULTS

Firstly, it was found that DBD can significantly alleviate ASP, which was manifested as a significant reduction in the atherosclerosis index, ratio of area for plaque to lumen, and vulnerability index. Afterwards, network pharmacology investigation identified quercetin and kaempferol as the primary active compounds in DBD anti-ASP. Key core targets mainly involved TP53, AKT1, IL-6 and TNF. The main action pathways included lipid and atherosclerosis, PI3K-Akt signaling, and other pathways. Subsequently, molecular docking and molecular dynamics simulation results confirmed the strong stability of the main active compounds with the key target. Finally, the cell validation experiment in vitro revealed that both quercetin and kaempferol could significantly inhibit RAW264.7 macrophage foaming formation induced by ox-LDL and improve its lipid metabolism disorder. Meanwhile, they could also significantly reverse ox-LDL induced abnormal expression of core protein predicted by network pharmacology in RAW264.7 foam cells. Further correlation analysis revealed that the improvement effect of quercetin and kaempferol on macrophage foaming had a close correlation with the inhibition of core protein expression.

CONCLUSION

DBD mainly utilized active ingredients such as quercetin and kaempferol, through regulating multiple targets like TP53, AKT1, IL-6 and TNF, to stabilize ASP.

FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping

Coarse-grained models have become ubiquitous in biomolecular modeling tasks aimed at studying slow dynamical processes such as protein folding and DNA hybridization. These models can considerably accelerate sampling but it remains challenging to accurately and efficiently restore all-atom detail to the coarse-grained trajectory, which can be vital for detailed understanding of molecular mechanisms and calculation of observables contingent on all-atom coordinates. In this work, we introduce FlowBack as a deep generative model employing a flow-matching objective to map samples from a coarse-grained prior distribution to an all-atom data distribution. We construct our prior distribution to be agnostic to the coarse-grained map and molecular type. A protein-specific model trained on ∼65k structures from the Protein Data Bank achieves state-of-the-art performance on structural metrics compared to previous generative and rules-based approaches in applications to static PDB structures, all-atom simulations of fast-folding proteins, and coarse-grained trajectories generated by a machine-learned force field. A DNA-protein model trained on ∼1.5k DNA-protein complexes achieves excellent reconstruction and generative capabilities on static DNA-protein complexes from the Protein Data Bank as well as on out-of-distribution coarse-grained dynamical simulations of DNA-protein complexation. FlowBack offers an accurate, efficient, and easy-to-use tool to recover all-atom structures from coarse-grained molecular simulations with higher robustness and fewer steric clashes than previous approaches. We make FlowBack freely available to the community as an open source Python package.

Deciphering the molecular mechanism underlying morphology transition in two-component DNA-protein cophase separation

Nucleic acid and protein co-condensates exhibit diverse morphologies crucial for fundamental cellular processes. Despite many previous studies that advanced our understanding of this topic, several interesting biophysical questions regarding the underlying molecular mechanisms remain. We investigated DNA and human transcription factor p53 co-condensates-a scenario where neither dsDNA nor the protein demonstrates phase-separation behavior individually. Through a combination of experimental assays and theoretical approaches, we elucidated: (1) the phase diagram of DNA-protein co-condensates at a certain observation time, identifying a phase transition between viscoelastic fluid and viscoelastic solid states, and a morphology transition from droplet-like to "pearl chain"-like co-condensates; (2) the growth dynamics of co-condensates. Droplet-like and "pearl chain"-like co-condensates share a common initial critical microscopic cluster size at the nanometer scale during the early stage of phase separation. These findings provide important insights into the biophysical mechanisms underlying multi-component phase separation within cellular environments.

Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Ligand Recognition and Activation Mechanism of the Alicarboxylic Acid Receptors

Endogenous ligands for alicarboxylic acid receptors are important metabolic intermediates that play a significant role in regulating body energy and maintaining homeostasis. However, the molecular mechanism of alicarboxylate ligand-mediated counterpart receptors is currently unclear. We resolve the active state structure of HCA2-niacin, and the structural analysis explains the mechanism of niacin selectivity in the alicarboxylic acid receptors family. Homology modeling, molecular dynamics simulation and mutagenesis experiments reveal different ligand recognition modes and activation mechanisms of the alicarboxylic acid receptors, analyze the flexibility of the binding pocket and elucidate the important role of disulfide bonds on receptor activation and ligand binding. These more detailed molecular mechanisms further elucidate the relevant mechanisms of human metabolism and provide key clues for subsequent drug development of alicarboxylic acid receptors.

Molecular mechanism of parental H3/H4 recycling at a replication fork

In chromatin replication, faithful recycling of histones from parental DNA to replicated strands is essential for maintaining epigenetic information across generations. A previous experiment has revealed that disrupting interactions between the N-terminal tail of Mcm2, a subunit in DNA replication machinery, and a histone H3/H4 tetramer perturb the recycling. However, the molecular pathways and the factors that regulate the ratio recycled to each strand and the destination location are yet to be revealed. Here, we performed molecular dynamics simulations of yeast DNA replication machinery, an H3/H4 tetramer, and replicated DNA strands. The simulations demonstrated that histones are recycled via Cdc45-mediated and unmediated pathways without histone chaperones, as our in vitro biochemical assays supported. Also, RPA binding regulated the ratio recycled to each strand, whereas DNA bending by Pol ε modulated the destination location. Together, the simulations provided testable hypotheses, which are vital for elucidating the molecular mechanisms of histone recycling.

Novel deoxyhypusine synthase (DHPS) inhibitors target hypusination-induced vasculogenic mimicry (VM) against malignant melanoma

Vasculogenic mimicry (VM) contributes factor to the poor prognosis of malignant melanoma. Developing deoxyhypusine synthase (DHPS) inhibitors against melanoma VM is clinically essential. In this study, we optimized and synthesized a series of compounds based on the candidate structure, and the hit compound 7k was identified through enzyme assay and cell viability inhibition screening. Both inside and outside the cell, 7k's ability to target DHPS and its high affinity were demonstrated. Molecular dynamics and point mutation indicated that mutations of K329 or V129 in DHPS abolish 7k's inhibitory activity. Using PCR arrays, solid-state antibody microarrays, and angiogenesis assays investigated 7k's impact on melanoma cells to reveal that DHPS regulates melanoma VM by promoting FGFR2 and c-KIT expression. Surprisingly, 7k was discovered to inhibit MC1R-mediated melanin synthesis in the zebrafish. Pharmacokinetic evaluations demonstrated 7k's favorable properties, and xenograft models evidenced its notable anti-melanoma efficacy, achieving a TGI of 73 %. These results highlighted DHPS as key in melanoma VM formation and confirmed 7k's potential as a novel anti-melanoma agent.

Fusion Dynamics and Size-Dependence of Droplet Microstructure in ssDNA-Mediated Protein Phase Separation

Biomolecular condensation involving proteins and nucleic acids has been recognized to play crucial roles in genome organization and transcriptional regulation. However, the biophysical mechanisms underlying the droplet fusion dynamics and microstructure evolution during the early stage of liquid-liquid phase separation (LLPS) remain elusive. In this work, we study the phase separation of linker histone H1, which is among the most abundant chromatin proteins, in the presence of single-stranded DNA (ssDNA) capable of forming a G-quadruplex by using molecular simulations and experimental characterization. We found that droplet fusion is a rather stochastic and kinetically controlled process. Productive fusion events are triggered by the formation of ssDNA-mediated electrostatic bridges within the droplet contacting zone. The droplet microstructure is size-dependent and evolves driven by maximizing the number of electrostatic contacts. We also showed that the folding of ssDNA to the G-quadruplex promotes LLPS by increasing the multivalency and strength of protein-DNA interactions. These findings provide deep mechanistic insights into the growth dynamics of biomolecular droplets and highlight the key role of kinetic control during the early stage of ssDNA-protein condensation.

Triclosan Dioxygenase: A Novel Two-component Rieske Nonheme Iron Ring-hydroxylating Dioxygenase Initiates Triclosan Degradation

The emerging contaminant triclosan (TCS) is widely distributed both in surface water and in wastewater and poses a threat to aquatic organisms and human health due to its resistance to degradation. The dioxygenase enzyme TcsAB has been speculated to perform the initial degradation of TCS, but its precise catalytic mechanism remains unclear. In this study, the function of TcsAB was elucidated using multiple biochemical and molecular biology methods. Escherichia coli BL21(DE3) heterologously expressing tcsAB from Sphingomonas sp. RD1 converted TCS to 2,4-dichlorophenol. TcsAB belongs to the group IA family of two-component Rieske nonheme iron ring-hydroxylating dioxygenases. The highest amino acid identity of TcsA and the large subunits of other dioxygenases in the same family was only 35.50%, indicating that TcsAB is a novel dioxygenase. Mutagenesis of residues near the substrate binding pocket decreased the TCS-degrading activity and narrowed the substrate spectrum, except for the TcsAF343A mutant. A meta-analysis of 1492 samples from wastewater treatment systems worldwide revealed that tcsA genes are widely distributed. This study is the first to report that the TCS-specific dioxygenase TcsAB is responsible for the initial degradation of TCS. Studying the microbial degradation mechanism of TCS is crucial for removing this pollutant from the environment.

Embracing exascale computing in nucleic acid simulations

This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.

pyMBE: The Python-based molecule builder for ESPResSo

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Rationally Engineering pH Adaptation of Acid-Induced Arginine Decarboxylase from Escherichia coli to Alkaline Environments to Efficiently Biosynthesize Putrescine

Acid-induced arginine decarboxylase AdiA is a typical homo-oligomeric protein biosynthesizing alkaline nylon monomer putrescine. However, upon loss of the AdiA decamer oligomeric state at neutral and alkaline conditions the activity also diminishes, obstructing the whole-cell biosynthesis of alkaline putrescine. Here, a structure cohesion strategy is proposed to change the pH adaptation of AdiA to alkaline environments based on the rational engineering of meridional and latitudinal oligomerization interfaces. After integrating substitutions of E467K at the latitudinal interface and H736E at the meridional channel interface, the structural stability of AdiA decamer and its substrate transport efficiency at neutral and alkaline conditions are improved. Finally, E467K_H736E is well adapted to neutral and alkaline environments (pH 7.0-9.0), and its enzymatic activity is 35-fold higher than that of wild AdiA at pH 8.0. Using E467K_H736E in the putrescine synthesis pathway, the titer of putrescine is up to 128.9 g·L-1 with a conversion of 0.94 mol·mol-1 in whole-cell catalysis. Additionally, the neutral pH adaptation of lysine decarboxylase, with a decamer structure similar to AdiA, is also improved using this cohesion strategy, providing an option for pH-adaptation engineering of other oligomeric decarboxylases.

Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations

Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Experimental and computational evidence that Calpain-10 binds to the carboxy terminus of NaV1.2 and NaV1.6

Voltage-gated sodium channels (NaV) are pivotal proteins responsible for initiating and transmitting action potentials. Emerging evidence suggests that proteolytic cleavage of sodium channels by calpains is pivotal in diverse physiological scenarios, including ischemia, brain injury, and neuropathic pain associated with diabetes. Despite this significance, the precise mechanism by which calpains recognize sodium channels, especially given the multiple calpain isoforms expressed in neurons, remains elusive. In this work, we show the interaction of Calpain-10 with NaV's C-terminus through a yeast 2-hybrid assay screening of a mouse brain cDNA library and in vitro by GST-pulldown. Later, we also obtained a structural and dynamic hypothesis of this interaction by modeling, docking, and molecular dynamics simulation. These results indicate that Calpain-10 interacts differentially with the C-terminus of NaV1.2 and NaV1.6. Calpain-10 interacts with NaV1.2 through domains III and T in a stable manner. In contrast, its interaction with NaV1.6 involves domains II and III, which could promote proteolysis through the Cys-catalytic site and C2 motifs.

Designer high-density lipoprotein particles enhance endothelial barrier function and suppress inflammation

High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium.

Modified host defence peptide GF19 slows TNT-mediated spread of corneal herpes simplex virus serotype I infection

Corneal HSV-1 infections are a leading cause of infectious blindness globally by triggering tissue damage due to the intense inflammation. HSV-1 infections are treated mainly with antiviral drugs that clear the infections but are inefficient as prophylactics. The body produces innate cationic host defence peptides (cHDP), such as the cathelicidin LL37. Various epithelia, including the corneal epithelium, express LL37. cHDPs can cause disintegration of pathogen membranes, stimulate chemokine production, and attract immune cells. Here, we selected GF17, a peptide containing the LL37 fragment with bioactivity but with minimal cytotoxicity, and added two cell-penetrating amino acids to enhance its activity. The resulting GF19 was relatively cell-friendly, inducing only partial activation of antigen presenting immune cells in vitro. We showed that HSV-1 spreads by tunneling nanotubes in cultured human corneal epithelial cells. GF19 given before infection was able to block infection, most likely by blocking viral entry. When cells were sequentially  exposed to viruses and GF19,  the infection was attenuated but not arrested, supporting the contention that the GF19 mode of action was to block viral entry. Encapsulation into silica nanoparticles allowed a more sustained release of GF19, enhancing its activity. GF19 is most likely suitable as a prevention rather than a virucidal treatment.

Glacier as a source of novel polyethylene terephthalate hydrolases

Polyethylene terephthalate (PET) is a major component of microplastic contamination globally, which is now detected in pristine environments including Polar and mountain glaciers. As a carbon-rich molecule, PET could be a carbon source for microorganisms dwelling in glacier habitats. Thus, glacial microorganisms may be potential PET degraders with novel PET hydrolases. Here, we obtained 414 putative PET hydrolase sequences by searching a global glacier metagenome dataset. Metagenomes from the Alps and Tibetan glaciers exhibited a higher relative abundance of putative PET hydrolases than those from the Arctic and Antarctic. Twelve putative PET hydrolase sequences were cloned and expressed, with one sequence (designated as GlacPETase) proven to degrade amorphous PET film with a similar performance as IsPETase, but with a higher thermostability. GlacPETase exhibited only 30% sequence identity to known active PET hydrolases with a novel disulphide bridge location and, therefore may represent a novel PET hydrolases class. The present work suggests that extreme carbon-poor environments may harbour a diverse range of known and novel PET hydrolases for carbon acquisition as an environmental adaptation mechanism.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

High-speed AFM imaging reveals DNA capture and loop extrusion dynamics by cohesin-NIPBL

3D chromatin organization plays a critical role in regulating gene expression, DNA replication, recombination, and repair. While initially discovered for its role in sister chromatid cohesion, emerging evidence suggests that the cohesin complex (SMC1, SMC3, RAD21, and SA1/SA2), facilitated by NIPBL, mediates topologically associating domains and chromatin loops through DNA loop extrusion. However, information on how conformational changes of cohesin-NIPBL drive its loading onto DNA, initiation, and growth of DNA loops is still lacking. In this study, high-speed atomic force microscopy imaging reveals that cohesin-NIPBL captures DNA through arm extension, assisted by feet (shorter protrusions), and followed by transfer of DNA to its lower compartment (SMC heads, RAD21, SA1, and NIPBL). While binding at the lower compartment, arm extension leads to the capture of a second DNA segment and the initiation of a DNA loop that is independent of ATP hydrolysis. The feet are likely contributed by the C-terminal domains of SA1 and NIPBL and can transiently bind to DNA to facilitate the loading of the cohesin complex onto DNA. Furthermore, high-speed atomic force microscopy imaging reveals distinct forward and reverse DNA loop extrusion steps by cohesin-NIPBL. These results advance our understanding of cohesin by establishing direct experimental evidence for a multistep DNA-binding mechanism mediated by dynamic protein conformational changes.

Bi-directional nucleosome sliding by the Chd1 chromatin remodeler integrates intrinsic sequence-dependent and ATP-dependent nucleosome positioning

Chromatin remodelers use a helicase-type ATPase motor to shift DNA around the histone core. Although not directly reading out the DNA sequence, some chromatin remodelers exhibit a sequence-dependent bias in nucleosome positioning, which presumably reflects properties of the DNA duplex. Here, we show how nucleosome positioning by the Chd1 remodeler is influenced by local DNA perturbations throughout the nucleosome footprint. Using site-specific DNA cleavage coupled with next-generation sequencing, we show that nucleosomes shifted by Chd1 can preferentially localize DNA perturbations - poly(dA:dT) tracts, DNA mismatches, and single-nucleotide insertions - about a helical turn outside the Chd1 motor domain binding site, super helix location 2 (SHL2). This phenomenon occurs with both the Widom 601 positioning sequence and the natural +1 nucleosome sequence from the Saccharomyces cerevisiae SWH1 gene. Our modeling indicates that localization of DNA perturbations about a helical turn outward from SHL2 results from back-and-forth sliding due to remodeler action on both sides of the nucleosome. Our results also show that barrier effects from DNA perturbations can be extended by the strong phasing of nucleosome positioning sequences.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.

Extension of the iSoLF implicit-solvent coarse-grained model for multicomponent lipid bilayers

iSoLF is a coarse-grained (CG) model for lipid molecules with the implicit-solvent approximation used in molecular dynamics (MD) simulations of biological membranes. Using the original iSoLF (iSoLFv1), MD simulations of lipid bilayers consisting of either POPC or DPPC and these bilayers, including membrane proteins, can be performed. Here, we improve the original model, explicitly treating the electrostatic interactions between different lipid molecules and adding CG particle types. As a result, the available lipid types increase to 30. To parameterize the potential functions of the new model, we performed all-atom MD simulations of each lipid at three different temperatures using the CHARMM36 force field and the modified TIP3P model. Then, we parameterized both the bonded and non-bonded interactions to fit the area per lipid and the membrane thickness of each lipid bilayer by using the multistate Boltzmann Inversion method. The final model reproduces the area per lipid and the membrane thickness of each lipid bilayer at the three temperatures. We also examined the applicability of the new model, iSoLFv2, to simulate the phase behaviors of mixtures of DOPC and DPPC at different concentrations. The simulation results with iSoLFv2 are consistent with those using Dry Martini and Martini 3, although iSoLFv2 requires much fewer computations. iSoLFv2 has been implemented in the GENESIS MD software and is publicly available.

Brownian dynamics simulations of mesoscale chromatin fibers

The relationship between chromatin architecture and function defines a central problem in biology and medicine. Many computational chromatin models with atomic, coarse-grained, mesoscale, and polymer resolution have been used to shed light onto the mechanisms that dictate genome folding and regulation of gene expression. The associated simulation techniques range from Monte Carlo to molecular, Brownian, and Langevin dynamics. Here, we present an efficient Compute Unified Device Architecture (CUDA) implementation of Brownian dynamics (BD) to simulate chromatin fibers at the nucleosome resolution with our chromatin mesoscale model. With the CUDA implementation for computer architectures with graphic processing units (GPUs), we significantly accelerate compute-intensive hydrodynamic tensor calculations in the BD simulations by massive parallelization, boosting the performance a hundred-fold compared with central processing unit calculations. We validate our BD simulation approach by reproducing experimental trends on fiber diffusion and structure as a function of salt, linker histone binding, and histone-tail composition, as well as Monte Carlo equilibrium sampling results. Our approach proves to be physically accurate with performance that makes feasible the study of chromatin fibers in the range of kb or hundreds of nucleosomes (small gene). Such simulations are essential to advance the study of biological processes such as gene regulation and aberrant genome-structure related diseases.

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC.

Optimized OPEP Force Field for Simulation of Crowded Protein Solutions

Macromolecular crowding has profound effects on the mobility of proteins, with strong implications on the rates of intracellular processes. To describe the dynamics of crowded environments, detailed molecular models are needed, capturing the structures and interactions arising in the crowded system. In this work, we present OPEPv7, which is a coarse-grained force field at amino-acid resolution, suited for rigid-body simulations of the structure and dynamics of crowded solutions formed by globular proteins. Using the OPEP protein model as a starting point, we have refined the intermolecular interactions to match the experimentally observed dynamical slowdown caused by crowding. The resulting force field successfully reproduces the diffusion slowdown in homogeneous and heterogeneous protein solutions at different crowding conditions. Coupled with the lattice Boltzmann technique, it allows the study of dynamical phenomena in protein assemblies and opens the way for the in silico rheology of protein solutions.

Insights from the molecular docking and simulation analysis of P38 MAPK phytochemical inhibitor complexes

It is of interest to develop p38α MAPK inhibitors. Docking, ADMET properties calculation, molecular dynamics, and MM-PBSA approaches were used to investigate the therapeutic potentials of p38α MAPK in complex with SB203580 (1A9U). The photo-molecules metergoline, withaphysacarpin, philadelphicalactone, canthin-6-one 9-glucoside, and SB-21600011 demonstrated high binding affinity compared to the reference drug. Furthermore, ADME profiles validated the drug-like properties of the prioritized phyto-compounds. Besides that, MD simulations were performed along with reference inhibitors for withaphysacarpin and metergoline to assess stability. Binding free energy calculations (MM-PBSA) revealed that metergoline and withaphysacarpin had estimated values (G) of 97.151 ± 21.023 kJ/mol and -82.084 ± 15.766 kJ/mol, respectively. In this study, metergoline and withaphysacarpin were found to have high affinity against p38α MAPK when compared to the reference compound SB 203580.

Highly Charged Proteins and Their Repulsive Interactions Antagonize Biomolecular Condensation

Biomolecular condensation is involved in various cellular processes; therefore, regulation of condensation is crucial to prevent deleterious protein aggregation and maintain a stable cellular environment. Recently, a class of highly charged proteins, known as heat-resistant obscure (Hero) proteins, was shown to protect other client proteins from pathological aggregation. However, the molecular mechanisms by which Hero proteins protect other proteins from aggregation remain unknown. In this study, we performed multiscale molecular dynamics (MD) simulations of Hero11, a Hero protein, and the C-terminal low-complexity domain (LCD) of the transactive response DNA-binding protein 43 (TDP-43), a client protein of Hero11, under various conditions to examine their interactions with each other. We found that Hero11 permeates into the condensate formed by the LCD of TDP-43 (TDP-43-LCD) and induces changes in conformation, intermolecular interactions, and dynamics of TDP-43-LCD. We also examined possible Hero11 structures in atomistic and coarse-grained MD simulations and found that Hero11 with a higher fraction of disordered region tends to assemble on the surface of the condensates. Based on the simulation results, we have proposed three possible mechanisms for Hero11's regulatory function: (i) In the dense phase, TDP-43-LCD reduces contact with each other and shows faster diffusion and decondensation due to the repulsive Hero11-Hero11 interactions. (ii) In the dilute phase, the saturation concentration of TDP-43-LCD is increased, and its conformation is relatively more extended and variant, induced by the attractive Hero11-TDP-43-LCD interactions. (iii) Hero11 on the surface of small TDP-43-LCD condensates can contribute to avoiding their fusion due to repulsive interactions. The proposed mechanisms provide new insights into the regulation of biomolecular condensation in cells under various conditions.

Optimization of parallel implementation of UNRES package for coarse-grained simulations to treat large proteins

We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size. Parallel performance of the code and comparison of its performance with that of AMBER, GROMACS and MARTINI 3 is presented.

Genome modeling: From chromatin fibers to genes

The intricacies of the 3D hierarchical organization of the genome have been approached by many creative modeling studies. The specific model/simulation technique combination defines and restricts the system and phenomena that can be investigated. We present the latest modeling developments and studies of the genome, involving models ranging from nucleosome systems and small polynucleosome arrays to chromatin fibers in the kb-range, chromosomes, and whole genomes, while emphasizing gene folding from first principles. Clever combinations allow the exploration of many interesting phenomena involved in gene regulation, such as nucleosome structure and dynamics, nucleosome-nucleosome stacking, polynucleosome array folding, protein regulation of chromatin architecture, mechanisms of gene folding, loop formation, compartmentalization, and structural transitions at the chromosome and genome levels. Gene-level modeling with full details on nucleosome positions, epigenetic factors, and protein binding, in particular, can in principle be scaled up to model chromosomes and cells to study fundamental biological regulation.

Atomic force microscopy measurements and model of DNA bending caused by binding of AraC protein

Atomic force microscopy (AFM) was used to conduct single-molecule imaging of protein/DNA complexes involved in the regulation of the arabinose operon of Escherichia coli. In the presence of arabinose, the transcription regulatory protein AraC binds to a 38 bp region consisting of the araI1 and araI2 half-sites. The domain positioning of full-length AraC, when bound to DNA, was not previously known. In this study, AraC was combined with 302 and 560 bp DNA and arabinose, deposited on a mica substrate, and imaged with AFM in air. High resolution images of 560 bp DNA, where bound protein was visible, showed that AraC induces a bend in the DNA with an angle 60° ± 12° with a median of 55°. These results are consistent with earlier gel electrophoresis measurements that measured the DNA bend angle based on migration rates. By using known domain structures of AraC, geometric constraints, and contacts determined from biochemical experiments, we developed a model of the tertiary and quaternary structure of DNA-bound AraC in the presence of arabinose. The DNA bend angle predicted by the model is in agreement with the measurement values. We discuss the results in view of other regulatory proteins that cause DNA bending and formation of the open complex to initiate transcription.

Modified Protein-Water Interactions in CHARMM36m for Thermodynamics and Kinetics of Proteins in Dilute and Crowded Solutions

Proper balance between protein-protein and protein-water interactions is vital for atomistic molecular dynamics (MD) simulations of globular proteins as well as intrinsically disordered proteins (IDPs). The overestimation of protein-protein interactions tends to make IDPs more compact than those in experiments. Likewise, multiple proteins in crowded solutions are aggregated with each other too strongly. To optimize the balance, Lennard-Jones (LJ) interactions between protein and water are often increased about 10% (with a scaling parameter, λ = 1.1) from the existing force fields. Here, we explore the optimal scaling parameter of protein-water LJ interactions for CHARMM36m in conjunction with the modified TIP3P water model, by performing enhanced sampling MD simulations of several peptides in dilute solutions and conventional MD simulations of globular proteins in dilute and crowded solutions. In our simulations, 10% increase of protein-water LJ interaction for the CHARMM36m cannot maintain stability of a small helical peptide, (AAQAA)₃ in a dilute solution and only a small modification of protein-water LJ interaction up to the 3% increase (λ = 1.03) is allowed. The modified protein-water interactions are applicable to other peptides and globular proteins in dilute solutions without changing thermodynamic properties from the original CHARMM36m. However, it has a great impact on the diffusive properties of proteins in crowded solutions, avoiding the formation of too sticky protein-protein interactions.

Electrostatic Map of the SARS-CoV-2 Virion Specifies Binding Sites of the Antiviral Cationic Photosensitizer

Electrostatics is an important part of virus life. Understanding the detailed distribution of charges over the surface of a virus is important to predict its interactions with host cells, antibodies, drugs, and different materials. Using a coarse-grained model of the entire viral envelope developed by D. Korkin and S.-J. Marrink's scientific groups, we created an electrostatic map of the external surface of SARS-CoV-2 and found a highly heterogeneous distribution of the electrostatic potential field of the viral envelope. Numerous negative patches originate mainly from negatively charged lipid domains in the viral membrane and negatively charged areas on the "stalks" of the spike (S) proteins. Membrane (M) and envelope (E) proteins with the total positive charge tend to colocalize with the negatively charged lipids. In the E protein pentamer exposed to the outer surface, negatively charged glutamate residues and surrounding lipids form a negative electrostatic potential ring around the channel entrance. We simulated the interaction of the antiviral octacationic photosensitizer octakis(cholinyl)zinc phthalocyanine with the surface structures of the entire model virion using the Brownian dynamics computational method implemented in ProKSim software (version r661). All mentioned negatively charged envelope components attracted the photosensitizer molecules and are thus potential targets for reactive oxygen generated in photosensitized reactions.

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.