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PreprintCited by 4Protein Structure and DynamicsEvolution and Genetic DynamicsRNA and protein synthesis mechanisms

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Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis

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Shukla, D, Martin, J, Morcos, F, Potoyan, DA·2024·Journal of Chemical Theory and Computation
DOI 10.1101/2024.10.08.617074PMC PMC11948321

Preprint — published as 10.1021/acs.jctc.4c01774 · Paper page

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Abstract

Protein evolution has produced enzymes that maintain stability and function across various thermal environments. While sequence variation, structural dynamics, and intermolecular interactions are known to influence an enzyme’s thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and structural dynamics shape patterns of cMDH’s thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of cMDH sequence space, enabling us to explore evolutionary pathways and predict fitness using direct coupling analysis (DCA). Structural predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. The integrative computational framework employed in this study provides powerful insights into protein adaptation at both sequence and structural levels, offering new perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties for biotechnological applications.

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Figures

Figure 1 Workflow for cMDH landscape generation and structural analysis. The workflow begins with multiple sequence alignments (MSA) encoded using one-hot encoding. These sequences are then processed through a Variational Autoencoder (VAE) to produce a two-dimensional representation of the sequence space. Fitness is assessed using a Potts Hamiltonian, with parameters infrared from Direct Coupling Analysis (DCA) of MSA data. The combination of these features generates a comprehensive evolutionary landscape containing natural and synthetic sequences. Predicted sequences are input into AlphaFold for three-dimensional structure prediction, including the cytosolic variant of malate dehydrogenase (cMDH). Molecular dynamics simulations are subsequently used to explore the dynamic properties of the structures, with contact analysis revealing variations in hydrophobic contact networks.
Figure 1 Workflow for cMDH landscape generation and structural analysis. The workflow begins with multiple sequence alignments (MSA) encoded using one-hot encoding. These sequences are then processed through a Variational Autoencoder (VAE) to produce a two-dimensional representation of the sequence space. Fitness is assessed using a Potts Hamiltonian, with parameters infrared from Direct Coupling Analysis (DCA) of MSA data. The combination of these features generates a comprehensive evolutionary landscape containing natural and synthetic sequences. Predicted sequences are input into AlphaFold for three-dimensional structure prediction, including the cytosolic variant of malate dehydrogenase (cMDH). Molecular dynamics simulations are subsequently used to explore the dynamic properties of the structures, with contact analysis revealing variations in hydrophobic contact networks.CC BY-NC-ND · PubMed Central
Figure 2 Clustering in the MDH evolutionary landscape. (A) An evolutionary landscape plot visualizes the distribution of Direct Coupling Analysis (DCA) scores for cytosolic malate dehydrogenase (cMDH) sequence variants generated by a Variational Autoencoder (VAE). The color gradient, ranging from blue to red, indicates DCA scores, with red representing regions of higher (unfavorable) scores. (B) The phylogenetic tree for cMDH, generated using Clustal Omega, is constructed from a distance matrix derived through pairwise sequence comparisons.
Figure 2 Clustering in the MDH evolutionary landscape. (A) An evolutionary landscape plot visualizes the distribution of Direct Coupling Analysis (DCA) scores for cytosolic malate dehydrogenase (cMDH) sequence variants generated by a Variational Autoencoder (VAE). The color gradient, ranging from blue to red, indicates DCA scores, with red representing regions of higher (unfavorable) scores. (B) The phylogenetic tree for cMDH, generated using Clustal Omega, is constructed from a distance matrix derived through pairwise sequence comparisons.CC BY-NC-ND · PubMed Central
Figure 3 Generated structures from the evolutionary landscape. scatter points represent the positions of generated structures within the evolutionary landscape. (A) Scatter points indicating the positions of generated structures in the landscape, where four types of structures (cMDH with active site, cMDH without active site, lactate dehydrogenase (LDH), and mitochondrial MDH) are clustered based on their RMSD values. (B) Cytosolic malate dehydrogenase (cMDH), mitochondrial malate dehydrogenase (MDH), and lactate dehydrogenase (LDH) structures aligned with generated sequence structures, all with root-mean-square deviation (RMSD) < 2.0 Å.
Figure 3 Generated structures from the evolutionary landscape. scatter points represent the positions of generated structures within the evolutionary landscape. (A) Scatter points indicating the positions of generated structures in the landscape, where four types of structures (cMDH with active site, cMDH without active site, lactate dehydrogenase (LDH), and mitochondrial MDH) are clustered based on their RMSD values. (B) Cytosolic malate dehydrogenase (cMDH), mitochondrial malate dehydrogenase (MDH), and lactate dehydrogenase (LDH) structures aligned with generated sequence structures, all with root-mean-square deviation (RMSD) < 2.0 Å.CC BY-NC-ND · PubMed Central
Figure 4 Relationship between DCA hamiltonian and normalized hydrophobic contacts in protein structures. (A) Scatter plot shows the correlation between DCA Hamiltonian values and the average degree of connectivity, for 400 protein structures generated from the VAE landscape. The red trend line indicates a decreasing degree of connectivity as the DCA Hamiltonian, plotted on the x-axis, increases. (B) Scatter plot depicts the correlation between DCA Hamiltonian values and the normalized number of hydrophobic contacts per residue across 400 protein structures generated from the VAE landscape. The red trend line indicates a decreasing number of hydrophobic contacts as the DCA Hamiltonian, plotted on the x-axis, increases.
Figure 4 Relationship between DCA hamiltonian and normalized hydrophobic contacts in protein structures. (A) Scatter plot shows the correlation between DCA Hamiltonian values and the average degree of connectivity, for 400 protein structures generated from the VAE landscape. The red trend line indicates a decreasing degree of connectivity as the DCA Hamiltonian, plotted on the x-axis, increases. (B) Scatter plot depicts the correlation between DCA Hamiltonian values and the normalized number of hydrophobic contacts per residue across 400 protein structures generated from the VAE landscape. The red trend line indicates a decreasing number of hydrophobic contacts as the DCA Hamiltonian, plotted on the x-axis, increases.CC BY-NC-ND · PubMed Central
Figure 5 Molecular dynamics analysis of side chain movements (RMSF) of cMDH across various species at ambient temperature. (A) Structure of cytosolic malate dehydrogenase (cMDH) highlighting functionally active residues. The catalytic loop (residues 91–105), region 1, is shown in red, while the additional catalytic region (residues 230–245), region 2, is depicted in blue. (B) Root Mean Square Fluctuation (RMSF) profiles at 298 K for cMDH from five species: Echinolittorina malaccana, Nerita yoldi, Mytilus californianus, Adamussium colbecki, and Nipponacmea radula. Peaks in RMSF at residues 91–105 and 230–245, marked in red and blue, respectively, indicate regions of significant fluctuation. (C) The contact map highlights that the region containing catalytically active residues forms long-range hydrophobic contacts. (D) Permanent hydrophobic contacts in region I (residues 80–110) and region II (residues 230–260) across different cMDH families, with psychrophilic enzymes on the left and thermophilic enzymes on the right, emphasizing differences in contact dynamics related to thermal adaptation. (E) The structures illustrate permanent hydrophobic contacts in region 1 (red) and region 2 (blue) of cMDH from Mytilus californianus and Adamussium colbecki.
Figure 5 Molecular dynamics analysis of side chain movements (RMSF) of cMDH across various species at ambient temperature. (A) Structure of cytosolic malate dehydrogenase (cMDH) highlighting functionally active residues. The catalytic loop (residues 91–105), region 1, is shown in red, while the additional catalytic region (residues 230–245), region 2, is depicted in blue. (B) Root Mean Square Fluctuation (RMSF) profiles at 298 K for cMDH from five species: Echinolittorina malaccana, Nerita yoldi, Mytilus californianus, Adamussium colbecki, and Nipponacmea radula. Peaks in RMSF at residues 91–105 and 230–245, marked in red and blue, respectively, indicate regions of significant fluctuation. (C) The contact map highlights that the region containing catalytically active residues forms long-range hydrophobic contacts. (D) Permanent hydrophobic contacts in region I (residues 80–110) and region II (residues 230–260) across different cMDH families, with psychrophilic enzymes on the left and thermophilic enzymes on the right, emphasizing differences in contact dynamics related to thermal adaptation. (E) The structures illustrate permanent hydrophobic contacts in region 1 (red) and region 2 (blue) of cMDH from Mytilus californianus and Adamussium colbecki.CC BY-NC-ND · PubMed Central
Figure 6 Comparison of changes in structural properties of generated proteins relative to their corresponding wild-type counterparts across varying Hamiltonian scores. Panels A, B, and C illustrate the differences in dynamic hydrophobic contacts (left), root-mean-square fluctuations (RMSF) across residues (center), and Hamiltonian energy differences (right) between the generated and wild-type structures. The data highlight structural and dynamic deviations influenced by Hamiltonian score variations.
Figure 6 Comparison of changes in structural properties of generated proteins relative to their corresponding wild-type counterparts across varying Hamiltonian scores. Panels A, B, and C illustrate the differences in dynamic hydrophobic contacts (left), root-mean-square fluctuations (RMSF) across residues (center), and Hamiltonian energy differences (right) between the generated and wild-type structures. The data highlight structural and dynamic deviations influenced by Hamiltonian score variations.CC BY-NC-ND · PubMed Central
Figure 7 Change in the dynamics and thermal stability along a path connecting the cMDH thermophile to mesophile clusters. (A) Root Mean Square Fluctuation (RMSF) profiles at 298 K for eight generated structures along a trajectory connecting the Echinolittorina, Nerita, and Mytilus families. (B) The shortest path between families in the landscape. (C) Correlation between the cosine similarity of root-mean-square fluctuation (RMSF) profiles of the wild-type structure and those of structures generated from varying Hamiltonian values near the clustering of wild-type sequences. (D) Hydrophobic contacts in both regions, residues 80–110 and residues 220–260.
Figure 7 Change in the dynamics and thermal stability along a path connecting the cMDH thermophile to mesophile clusters. (A) Root Mean Square Fluctuation (RMSF) profiles at 298 K for eight generated structures along a trajectory connecting the Echinolittorina, Nerita, and Mytilus families. (B) The shortest path between families in the landscape. (C) Correlation between the cosine similarity of root-mean-square fluctuation (RMSF) profiles of the wild-type structure and those of structures generated from varying Hamiltonian values near the clustering of wild-type sequences. (D) Hydrophobic contacts in both regions, residues 80–110 and residues 220–260.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Shukla, D., Martin, J., Morcos, F., & Potoyan, D.
Affiliations
  • Iowa State University (US)
  • The University of Texas at Dallas (US)
ORCID
Venue
Journal of Chemical Theory and Computation
Publisher
openRxiv
Published
2024-10-11
Volume
21
Issue
6
Pages
3277-3287
ISSN
1549-9618 (ISSN-L)
DOI
10.1101/2024.10.08.617074
PMC
PMC11948321
OpenAlex
W4408413780
Cited by
4
References
1
Citations by year
2026: 4
FWCI
1.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Chemical Theory and Computation (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Institute of General Medical Sciences (R35GM133631, R35GM138243)
  • National Institute of Allergy and Infectious Diseases (R01AI178692)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
cytosolic malate dehydrogenase, thermal adaptation, protein thermal stability, coevolution, variational autoencoder, direct coupling analysis, hydrophobic interactions, conformational flexibility, citric acid cycle enzymes, mutational pathways, protein evolution
MeSH terms
Deep Learning, Cytosol, Enzyme Stability, Malate Dehydrogenase, Protein Conformation, Temperature, Evolution, Molecular, Molecular Dynamics Simulation, Hydrophobic and Hydrophilic Interactions

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Shukla, D., Martin, J., Morcos, F., & Potoyan, D. (2024). Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis. Journal of Chemical Theory and Computation. https://doi.org/10.1101/2024.10.08.617074