Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

Structural insights into the pathogenicity of point mutations in human acyl-CoA dehydrogenase homotetramers

Acyl-CoA dehydrogenase deficiency (ACAD) is an inherited and potentially fatal disorder with variable clinical symptoms. The relationship between pathogenicity and deleterious point mutations is investigated here in ACAD structures of short (SCAD) and medium-chain (MCAD) types. Structures and dynamic features of native and mutant forms of enzymes models were compared. A total of 2.88 µs molecular dynamics simulations were performed at four different temperatures. Total energy, RMSD, protein ligand interactions and affinity, RMSF measures, secondary structure changes, and important interactions were studied. Mutations in the three main domains of ACADs are pathogenic, while those located at linker turns are not. Mutations affect mostly tetramer formations, secondary structures, and many contacts and interactions. In R206H (MCAD mutant) which is experimentally known to cause a huge turnover decrease, the lack of a single H-bond between substrate and FAD was observed. Secondary structures showed temperature-dependent changes, and SCAD activity was found to be highly correlated to the enzyme helix 3-10 content. Finally, RMSF patterns pointed to one important loop that maintains the substrate close to the active site and is a cause of substrate wobbling upon mutation. Despite similar structure, function, and cellular location, SCAD and MCAD may have different optimum temperatures that are related to the structure taken at that specific temperature. In conclusion, new insight has been provided on the effect of various SCAD and MCAD pathogenic mutations on the structure and dynamical features of the enzymes.

Protein dynamics underlying allosteric regulation

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.

Conformational transitions of the HIV-1 Gag polyprotein upon multimerization and gRNA binding

During the HIV-1 assembly process, the Gag polyprotein multimerizes at the producer cell plasma membrane, resulting in the formation of spherical immature virus particles. Gag-genomic RNA (gRNA) interactions play a crucial role in the multimerization process, which is yet to be fully understood. We performed large-scale all-atom molecular dynamics simulations of membrane-bound full-length Gag dimer, hexamer, and 18-mer. The inter-domain dynamic correlation of Gag, quantified by the heterogeneous elastic network model applied to the simulated trajectories, is observed to be altered by implicit gRNA binding, as well as the multimerization state of the Gag. The lateral dynamics of our simulated membrane-bound Gag proteins, with and without gRNA binding, agree with prior experimental data and help to validate our simulation models and methods. The gRNA binding is observed to affect mainly the SP1 domain of the 18-mer and the matrix-capsid linker domain of the hexamer. In the absence of gRNA binding, the independent dynamical motion of the nucleocapsid domain results in a collapsed state of the dimeric Gag. Unlike stable SP1 helices in the six-helix bundle, without IP6 binding, the SP1 domain undergoes a spontaneous helix-to-coil transition in the dimeric Gag. Together, our findings reveal conformational switches of Gag at different stages of the multimerization process and predict that the gRNA binding reinforces an efficient binding surface of Gag for multimerization, and also regulates the dynamic organization of the local membrane region itself.

Representing structures of the multiple conformational states of proteins

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Handling DNA malfunctions by unsupervised machine learning model

The cell cycle is a rich field for research, especially, the DNA damage. DNA damage, which happened naturally or as a result of environmental influences causes change in the chemical structure of DNA. The extent of DNA damage has a significant impact on the fate of the cell in later stages. In this paper, we introduced an Unsupervised Machine learning Model for DNA Damage Diagnosis and Analysis. Mainly, we employed K-means clustering unsupervised machine learning algorithms. Unsupervised algorithms commonly draw conclusions from datasets by solely utilizing input vectors, disregarding any known or labeled outcomes. The model provided deep insight about DNA damage and exposes the protein levels for proteins when work together in sub-network model to deal with DNA damage occurrence, the unsupervised artificial model explained the sub-network biological model activities in regard to the changing in their concentrations in several clusters, they have been grouped in such as (0 - no damage, 1 - low, 2 - medium, 3 - high, and 4 - excess) DNA damage clusters. The results provided a rational and persuasive explanation for numerous important phenomena, including the oscillation of the protein p53, in a clear and understandable manner. Which is encouraging since it demonstrates that the K-means clustering approach can be easily applied to many similar biological systems, which aids in better understanding the key dynamics of these systems.

Slc11 Synapomorphy: A Conserved 3D Framework Articulating Carrier Conformation Switch

Transmembrane carriers of the Slc11 family catalyze proton (H+)-dependent uptake of divalent metal ions (Me2+) such as manganese and iron-vital elements coveted during infection. The Slc11 mechanism of high-affinity Me2+ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me2+ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution, Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outwardly open (OO) to inwardly open (IO) carriers. In silico mutagenesis targeting (i) a set of (evolutionarily coupled) sites that may define Slc11 function (putative synapomorphy) and (ii) residues from networked communities evolving during MCb transition indicates that Slc11 synapomorphy primarily instructs a Me2+-selective conformation switch which unlocks carrier inner gate and contributes to Me2+ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to an altered intramolecular H+ network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to an OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, with extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks the emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying that it constitutes a 3D network of residues articulating a Me2+-selective carrier conformation switch which is maintained in fast-evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics.

Conformational transitions of the HIV-1 Gag polyprotein upon multimerization and gRNA binding

During the HIV-1 assembly process, the Gag polyprotein multimerizes at the producer cell plasma membrane, resulting in the formation of spherical immature virus particles. Gag-gRNA interactions play a crucial role in the multimerization process, which is yet to be fully understood. We have performed large-scale all-atom molecular dynamics simulations of membrane-bound full-length Gag dimer, hexamer, and 18-mer. The inter-domain dynamic correlation of Gag, quantified by the heterogeneous elastic network model (hENM) applied to the simulated trajectories, is observed to be altered by implicit gRNA binding, as well as the multimerization state of the Gag. The lateral dynamics of our simulated membrane-bound Gag proteins, with and without gRNA binding, agree with prior experimental data and help to validate our simulation models and methods. The gRNA binding is observed to impact mainly the SP1 domain of the 18-mer and the MA-CA linker domain of the hexamer. In the absence of gRNA binding, the independent dynamical motion of the NC domain results in a collapsed state of the dimeric Gag. Unlike stable SP1 helices in the six-helix bundle, without IP6 binding, the SP1 domain undergoes a spontaneous helix-to-coil transition in the dimeric Gag. Together, our findings reveal conformational switches of Gag at different stages of the multimerization process and predict that the gRNA binding reinforces an efficient binding surface of Gag for multimerization, as well as regulates the dynamic organization of the local membrane region itself.

Significance

Gag(Pr 55 Gag ) polyprotein orchestrates many essential events in HIV-1 assembly, including packaging of the genomic RNA (gRNA) in the immature virion. Although various experimental techniques, such as cryo-ET, X-ray, and NMR, have revealed structural properties of individual domains in the immature Gag clusters, structural and biophysical characterization of a full-length Gag molecule remains a challenge for existing experimental techniques. Using atomistic molecular dynamics simulations of the different model systems of Gag polyprotein, we present here a detailed structural characterization of Gag molecules in different multimerization states and interrogate the synergy between Gag-Gag, Gag-membrane, and Gag-gRNA interactions during the viral assembly process.

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions

The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.