The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program

Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.

Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged by the fact that approximately half of the human proteome is made up of proteins that do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered proteins (IDPs), are involved in numerous physiological functions and are associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), and type 2 diabetes. Targeting these proteins is challenging for two reasons: i) we need to preserve their physiological functions, and ii) drug design by molecular docking is not possible due to the lack of reliable starting conditions. Faced with this challenge, the solutions proposed by artificial intelligence (AI) such as AlphaFold are clearly unsuitable. Instead, we suggest an innovative approach consisting of mimicking, in short synthetic peptides, the conformational flexibility of IDPs. These peptides, which we call adaptive peptides, are derived from the domains of IDPs that become structured after interacting with a ligand. Adaptive peptides are designed with the aim of selectively antagonizing the harmful effects of IDPs, without targeting them directly but through selected ligands, without affecting their physiological properties. This "target the target, not the arrow" strategy is promised to open a new route to drug discovery for currently undruggable proteins.

AlphaFold and what is next: bridging functional, systems and structural biology

INTRODUCTION

The DeepMind's AlphaFold (AF) has revolutionized biomedical and biocience research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.

AREAS COVERED

In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide, and their interactions in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar.

EXPERT OPINION

While significant progress has been made to enhance AF's modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.

Structural Biology for Target Identification and Validation

Structural biology is catalyzing a paradigm shift in drug discovery towards rational approaches in target identification and validation. Leveraging structural insights obtained through cryo-EM or X-ray crystallography not only enhances the efficiency of drug discovery projects in terms of time and cost, but also significantly improves the likelihood of achieving market approval.Initiating a successful project necessitates more than just a robust package for target credentialing; it demands a comprehensive strategy for the identification and optimization of potential drugs. The critical evaluation of target druggability is markedly enhanced when supported by experimentally derived structural information. This nuanced approach ensures a more thorough understanding of the technical feasibility of drug development from the project's inception.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Advancing Membrane-Associated Protein Docking with Improved Sampling and Scoring in Rosetta

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as rigid (RMSDUB < 1.2 Å), moderately flexible (RMSDUB ∈ [1.2, 2.2] Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Conformational analysis of the IQSEC2 protein by statistical thermodynamics

Mutations in the IQSEC2 gene result in severe intellectual disability, epilepsy and autism. The primary function of IQSEC2 is to serve as a guanine exchange factor (GEF) controlling the activation of ARF6 which in turn mediates membrane trafficking and synaptic connections between neurons. As IQSEC2 is a large intrinsically disordered protein little is known of the structure of the protein and how this influences its function. Understanding this structure and function relationship is critical for the development of novel therapies to treat IQSEC2 disease. We therefore sought to identify IQSEC2 conformers in unfolded and folded states and analyze how conformers differ when binding to ARF6 and thereby influence GEF catalysis. We simulated the folding process of IQSEC2 by accelerated molecular dynamics (aMD). Following the ensemble method of Gibbs, we proposed that the number of microstates in the ensemble replicating a protein macroscopic system is the total number of MD snapshots sampled on the production MD trajectory. We divided the entire range of reaction coordinate into a series of consecutive, non-overlapping bins. Thermal fluctuations of biomolecules in local equilibrium states are Gaussian in form. To predict the free energy and entropy of different conformational states using statistical thermodynamics, the density of states was estimated taking into account how many MD snapshots constitute each conformational state. IQSEC2 dimers derived from the most stable folded and unfolded conformers of IQSEC2 were generated by protein-protein docking and then used to construct IQSEC2-ARF6 encounter complexes. We suggest that IQSEC2 folding and dimerization are two competing processes that may be used by nature to regulate the process of GDP exchange on ARF6 catalyzed by IQSEC2.

Light sensitivity in Beroidae ctenophores: Insights from laboratory studies and genomics

Light detection underlies a variety of animal behaviors, including those related to spatial orientation, feeding, avoidance of predators, and reproduction. Ctenophores are likely the oldest animal group in which light sensitivity based on opsins evolved, so they may still have the ancestral molecular mechanisms for photoreception. However, knowledge about ctenophore photosensitivity, associated morphological structures, molecular mechanisms involved, and behavioral reactions is limited and fragmented. We present the initial experiments on the responses of adult Beroe ovata to high-intensity light exposure with different spectra and photosensitivity in various parts of the animal's body. Ctenophores have shown a consistent behavioral response when their aboral organ is exposed to a household-grade laser in the violet spectrum. To investigate the genes responsible for the photosensitivity of Beroidae, we have analyzed transcriptome and genome-wide datasets. We identified three opsins in Beroe that are homologous to those found in Mnemiopsis leidyi (Lobata) and Pleurobrachia bachei (Cydippida). These opsins form clades Ctenopsin1, 2, and 3, respectively. Ctenopsin3 is significantly distinct from other ctenophore opsins and clustered outside the main animal opsin groups. The Ctenopsin1 and Ctenopsin2 groups are sister clusters within the canonical animal opsin tree. These two groups could have originated from gene duplication in the common ancestor of the species we studied and then developed independently in different lineages of Ctenophores. So far, there is no evidence of additional expansion of the opsin family in ctenophore evolution. The involvement of ctenophore opsins in photoreception is discussed by analyzing their protein structures.

Insights into AlphaFold's breakthrough in neurodegenerative diseases

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

The determination of a protein's structure is often a first step towards the development of a mechanistic understanding of its function. Considerable advances in computational protein structure prediction have been made in recent years, with AlphaFold2 (AF2) emerging as the primary tool used by researchers for this purpose. While AF2 generally predicts accurate structures of folded proteins, we present here a case where AF2 incorrectly predicts the structure of a small, folded and compact protein with high confidence. This protein, pro-interleukin-18 (pro-IL-18), is the precursor of the cytokine IL-18. Interestingly, the structure of pro-IL-18 predicted by AF2 matches that of the mature cytokine, and not the corresponding experimentally determined structure of the pro-form of the protein. Thus, while computational structure prediction holds immense promise for addressing problems in protein biophysics, there is still a need for experimental structure determination, even in the context of small well-folded, globular proteins.

Structure elucidation of a multi-modular recombinant endoglucanase, AtGH9C-CBM3A-CBM3B from Acetivibrio thermocellus ATCC 27405 and its substrate binding analysis

Cellulases from GH9 family show endo-, exo- or processive endocellulase activity, but the reason behind the variation is unclear. A GH9 recombinant endoglucanase, AtGH9C-CBM3A-CBM3B from Acetivibrio thermocellus was structurally characterized for conformation, binding and dynamics assessment. Modeled AtGH9C-CBM3A-CBM3B depicted (α/α)6-barrel structure with Asp98, Asp101 and Glu489 acting as catalytic triad. CD results revealed 25.2 % α-helix, 18.4 % β-sheet and rest 56.4 % of random coils, corroborating with predictions from PSIPRED and SOPMA. MD simulation of AtGH9C-CBM3A-CBM3B bound cellotetraose showed structural stability and global compactness with lowered RMSD values (1.5 nm) as compared with only AtGH9C-CBM3A-CBM3B (1.8 nm) for 200 ns. Higher fluctuation in RMSF values in far-positioned CBM3B pointed to its redundancy in substrate binding. Docking studies showed maximum binding with cellotetraose (ΔG = -5.05 kcal/mol), with reduced affinity towards ligands with degree of polymerization (DP) lower (DP < 4) or higher than 4 (DP > 4). Processivity index displayed the enzyme to be processive with loop 3 (342-379 aa) possibly blocking the non-reducing end of cellulose chain, resulting in cellotetraose release. SAXS analysis of AtGH9C-CBM3A-CBM3B at 5 mg/mL displayed monodispersed state with fist-and-elbow shape in solution. Negative zeta potential of -24 mV at 5 mg/mL indicated stability and free from aggregation.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

What can AlphaFold do for antimicrobial amyloids?

Amyloids, protein, and peptide assemblies in various organisms are crucial in physiological and pathological processes. Their intricate structures, however, present significant challenges, limiting our understanding of their functions, regulatory mechanisms, and potential applications in biomedicine and technology. This study evaluated the AlphaFold2 ColabFold method's structure predictions for antimicrobial amyloids, using eight antimicrobial peptides (AMPs), including those with experimentally determined structures and AMPs known for their distinct amyloidogenic morphological features. Additionally, two well-known human amyloids, amyloid-β and islet amyloid polypeptide, were included in the analysis due to their disease relevance, short sequences, and antimicrobial properties. Amyloids typically exhibit tightly mated β-strand sheets forming a cross-β configuration. However, certain amphipathic α-helical subunits can also form amyloid fibrils adopting a cross-α structure. Some AMPs in the study exhibited a combination of cross-α and cross-β amyloid fibrils, adding complexity to structure prediction. The results showed that the AlphaFold2 ColabFold models favored α-helical structures in the tested amyloids, successfully predicting the presence of α-helical mated sheets and a hydrophobic core resembling the cross-α configuration. This implies that the AI-based algorithms prefer assemblies of the monomeric state, which was frequently predicted as helical, or capture an α-helical membrane-active form of toxic peptides, which is triggered upon interaction with lipid membranes.

Molecular modeling of ARF6 dysregulation caused by mutations in IQSEC2

IQSEC2 gene mutations are associated with epilepsy, autism, and intellectual disability. The primary function IQSEC2, mediated via its Sec 7 domain, is to act as a guanine nucleotide exchange factor for ARF6. We sought to develop a molecular model, which may explain the aberrant Sec 7 activity on ARF6 of different human IQSEC2 mutations. We integrated experimental data of IQSEC2 mutants with protein structure prediction by the RaptorX server combined with molecular modeling and molecular dynamics simulations. Normally, apocalmodulin (apoCM) binds to IQSEC2 resulting in its N-terminal fragment inhibiting access of its Sec 7 domain to ARF6. An increase in Ca2+ concentration destabilizes the interaction of IQSEC2 with apoCM and removes steric hindrance of Sec 7 binding with ARF6. Mutations at amino acid residue 350 of IQSEC2 result in loss of steric hindrance of Sec 7 binding with ARF6 leading to constitutive activation of ARF6 by Sec 7. On the other hand, a mutation at amino acid residue 359 of IQSEC2 results in constitutive hindrance of Sec 7 binding to ARF6 leading to the loss of the ability of IQSEC2 to activate ARF6. These studies provide a model for dysregulation of IQSEC2 Sec 7 activity by mutant IQSEC2 proteins.Communicated by Ramaswamy H. Sarma.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

Tld1 is a regulator of triglyceride lipolysis that demarcates a lipid droplet subpopulation

Cells store lipids in the form of triglyceride (TG) and sterol ester (SE) in lipid droplets (LDs). Distinct pools of LDs exist, but a pervasive question is how proteins localize to and convey functions to LD subsets. Here, we show that the yeast protein YDR275W/Tld1 (for TG-associated LD protein 1) localizes to a subset of TG-containing LDs and reveal it negatively regulates lipolysis. Mechanistically, Tld1 LD targeting requires TG, and it is mediated by two distinct hydrophobic regions (HRs). Molecular dynamics simulations reveal that Tld1's HRs interact with TG on LDs and adopt specific conformations on TG-rich LDs versus SE-rich LDs in yeast and human cells. Tld1-deficient yeast display no defect in LD biogenesis but exhibit elevated TG lipolysis dependent on lipase Tgl3. Remarkably, overexpression of Tld1, but not LD protein Pln1/Pet10, promotes TG accumulation without altering SE pools. Finally, we find that Tld1-deficient cells display altered LD mobilization during extended yeast starvation. We propose that Tld1 senses TG-rich LDs and regulates lipolysis on LD subpopulations.

Spartin is a Lipid Transfer Protein That Facilitates Lipid Droplet Turnover

One means by which cells reutilize neutral lipids stored in lipid droplets is to degrade them by autophagy. This process involves spartin, mutations of which cause the rare inherited disorder Troyer syndrome (or spastic paraplegia-20, SPG20). A recently published paper from the team led by Karin Reinsich (Yale) suggests that the molecular function of spartin and its unique highly conserved "senescence" domain is as a lipid transfer protein. Spartin binds to and transfers all lipid species found in lipid droplets, from phospholipids to triglycerides and sterol esters. This lipid transfer activity correlates with spartin's ability to sustain lipid droplet turnover. The senescence domain poses an intriguing question around the wide range of its cargoes, but intriguingly it has yet to yield up its secrets because attempts at crystallization failed and AlphaFold's prediction is unconvincing.

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.

Tld1 is a novel regulator of triglyceride lipolysis that demarcates a lipid droplet subpopulation

Cells store lipids in the form of triglyceride (TG) and sterol-ester (SE) in lipid droplets (LDs). Distinct pools of LDs exist, but a pervasive question is how proteins localize to and convey functions to LD subsets. Here, we show the yeast protein YDR275W/Tld1 (for TG-associated LD protein 1) localizes to a subset of TG-containing LDs, and reveal it negatively regulates lipolysis. Mechanistically, Tld1 LD targeting requires TG, and is mediated by two distinct hydrophobic regions (HRs). Molecular dynamics simulations reveal Tld1 HRs interact with TG on LDs and adopt specific conformations on TG-rich LDs versus SE-rich LDs in yeast and human cells. Tld1-deficient yeast display no defect in LD biogenesis, but exhibit elevated TG lipolysis dependent on lipase Tgl3. Remarkably, over-expression of Tld1, but not LD protein Pln1/Pet10, promotes TG accumulation without altering SE pools. Finally, we find Tld1-deficient cells display altered LD mobilization during extended yeast starvation. We propose Tld1 senses TG-rich LDs and regulates lipolysis on LD subpopulations.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Lipid rafts and human diseases: why we need to target gangliosides

Gangliosides are functional components of membrane lipid rafts that control critical functions in cell communication. Many pathologies involve raft gangliosides, which therefore represent an approach of choice for developing innovative therapeutic strategies. Beginning with a discussion of what a disease is (and is not), this review lists the major human pathologies that involve gangliosides, which includes cancer, diabetes, and infectious and neurodegenerative diseases. In most cases, the problem is due to a protein whose binding to gangliosides either creates a pathological condition or impairs a physiological function. Then, I draw up an inventory of the different molecular mechanisms of protein-ganglioside interactions. I propose to classify the ganglioside-binding domains of proteins into four categories, which I name GBD-1, GBD-2, GBD-3, and GBD-4. This structural and functional classification could help to rationalize the design of innovative molecules capable of disrupting the binding of selected proteins to gangliosides without generating undesirable effects. The biochemical specificities of gangliosides expressed in the human brain must also be taken into account to improve the reliability of animal models (or any animal-free alternative) of Alzheimer's and Parkinson's diseases.

Comparison, Analysis, and Molecular Dynamics Simulations of Structures of a Viral Protein Modeled Using Various Computational Tools

The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein structures, with or without structural templates. In this study, we used three neural network-based de novo modeling approaches-AlphaFold2 (AF2), Robetta-RoseTTAFold (Robetta), and transform-restrained Rosetta (trRosetta)-and two template-based tools-the Molecular Operating Environment (MOE) and iterative threading assembly refinement (I-TASSER)-to construct the structure of a viral capsid protein, hepatitis C virus core protein (HCVcp), whose structure have not been fully resolved by laboratory techniques. Templates with sufficient sequence identity for the homology modeling of complete HCVcp are currently unavailable. Therefore, we performed domain-based homology modeling for MOE simulations. The templates for each domain were obtained through sequence-based searches on NCBI and the Protein Data Bank. Then, the modeled domains were assembled to construct the complete structure of HCVcp. The full-length structure and two truncated forms modeled using various computational tools were compared. Molecular dynamics (MD) simulations were performed to refine the structures. The root mean square deviation of backbone atoms, root mean square fluctuation of Cα atoms, and radius of gyration were calculated to monitor structural changes and convergence in the simulations. The model quality was evaluated through ERRAT and phi-psi plot analysis. In terms of the initial prediction for protein modeling, Robetta and trRosetta outperformed AF2. Regarding template-based tools, MOE outperformed I-TASSER. MD simulations resulted in compactly folded protein structures, which were of good quality and theoretically accurate. Thus, the predicted structures of certain proteins must be refined to obtain reliable structural models. MD simulation is a promising tool for this purpose.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

Electrostatic Surface Potential as a Key Parameter in Virus Transmission and Evolution: How to Manage Future Virus Pandemics in the Post-COVID-19 Era

Virus-cell interactions involve fundamental parameters that need to be considered in strategies implemented to control viral outbreaks. Among these, the surface electrostatic potential can give valuable information to deal with new epidemics. In this article, we describe the role of this key parameter in the hemagglutination of red blood cells and in the co-evolution of synaptic receptors and neurotransmitters. We then establish the functional link between lipid rafts and the electrostatic potential of viruses, with special emphasis on gangliosides, which are sialic-acid-containing, electronegatively charged plasma membrane components. We describe the common features of ganglioside binding domains, which include a wide variety of structures with little sequence homology but that possess key amino acids controlling ganglioside recognition. We analyze the role of the electrostatic potential in the transmission and intra-individual evolution of HIV-1 infections, including gatekeeper and co-receptor switch mechanisms. We show how to organize the epidemic surveillance of influenza viruses by focusing on mutations affecting the hemagglutinin surface potential. We demonstrate that the electrostatic surface potential, by modulating spike-ganglioside interactions, controls the hemagglutination properties of coronaviruses (SARS-CoV-1, MERS-CoV, and SARS-CoV-2) as well as the structural dynamics of SARS-CoV-2 evolution. We relate the broad-spectrum antiviral activity of repositioned molecules to their ability to disrupt virus-raft interactions, challenging the old concept that an antibiotic or anti-parasitic cannot also be an antiviral. We propose a new concept based on the analysis of the electrostatic surface potential to develop, in real time, therapeutic and vaccine strategies adapted to each new viral epidemic.

Structural Basis of Botulinum Toxin Type F Binding to Glycosylated Human SV2A: In Silico Studies at the Periphery of a Lipid Raft

Botulinum neurotoxins are the deadliest microbial neurotoxins in humans, with a lethal dose of 1 ng/kg. Incidentally, these neurotoxins are also widely used for medical and cosmetic purposes. However, little is known about the molecular mechanisms that control binding of botulinum neurotoxin type F1 (BoNT/F1) to its membrane receptor, glycosylated human synaptic vesicle glycoprotein A (hSV2Ag). To elucidate these mechanisms, we performed a molecular dynamics simulation (MDS) study of initial binding kinetics of BoNT/F1 to SV2A. Since this toxin also interacts with gangliosides, the simulations were performed at the periphery of a lipid raft in the presence of both SV2A and gangliosides. Our study suggested that interaction of BoNT/F1 with SV2A is exclusively mediated by N-glycan moiety of SV2A, which interacts with aromatic residues Y898, Y910, F946, Y1059 and H1273 of this toxin. Thus, in contrast with botulinum neurotoxin A1 (BoNT/A1), BoNT/F1 does not interact with protein content of SV2A. We attributed this incapability to a barrage effect exerted by neurotoxin residues Y1132, Q1133 and K1134, which prevent formation of long-lasting intermolecular hydrogen bonds. We also provided structural elements that suggest that BoNT/F1 uses the strategy of BoNT/A1 combined with the strategy of botulinum neurotoxin type E to bind N-glycan of its glycoprotein receptor. Overall, our study opened a gate for design of a universal inhibitor aimed at disrupting N-glycan-toxin interactions and for bioengineering of a BoNT/F1 protein that may be able to bind protein content of synaptic vesicle glycoprotein for therapeutic purposes.

A Vaccine Strategy Based on the Identification of an Annular Ganglioside Binding Motif in Monkeypox Virus Protein E8L

The recent outbreak of Monkeypox virus requires the development of a vaccine specifically directed against this virus as quickly as possible. We propose here a new strategy based on a two-step analysis combining (i) the search for binding domains of viral proteins to gangliosides present in lipid rafts of host cells, and (ii) B epitope predictions. Based on previous studies of HIV and SARS-CoV-2 proteins, we show that the Monkeypox virus cell surface-binding protein E8L possesses a ganglioside-binding motif consisting of several subsites forming a ring structure. The binding of the E8L protein to a cluster of gangliosides GM1 mimicking a lipid raft domain is driven by both shape and electrostatic surface potential complementarities. An induced-fit mechanism unmasks selected amino acid side chains of the motif without significantly affecting the secondary structure of the protein. The ganglioside-binding motif overlaps three potential linear B epitopes that are well exposed on the unbound E8L surface that faces the host cell membrane. This situation is ideal for generating neutralizing antibodies. We thus suggest using these three sequences derived from the E8L protein as immunogens in a vaccine formulation (recombinant protein, synthetic peptides or genetically based) specific for Monkeypox virus. This lipid raft/ganglioside-based strategy could be used for developing therapeutic and vaccine responses to future virus outbreaks, in parallel to existing solutions.