Biology subjects

Haldane, A.

Publications and source records attributed to Haldane, A..

3 recordsLinked to original sources

bioRxiv2022

Evolutionary Divergence in the Conformational Landscapes of Tyrosine vs Serine/Threonine Kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved "type-II inhibitors" in the treatment of cancers. Tyrosine Kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while Serine/Threonine Kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale ([~]17-20[A]) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical "folded activation loop" DFG-out conformation relative to the active conformation that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly "anchored" against the catalytic loop motif in the active conformation, and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and pharmacological implications for the target selectivity of type-II inhibitors.

biophysics↗

bioRxiv2021

The role of epistasis in determining the fitness landscape of HIV proteins

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance with unambiguous signatures of epistasis best seen in the comparison of a maximum entropy sequence co-variation (Potts) model predicted and experimental HIV sequence "prevalences" when expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, the evidence for epistasis based on experimental measures of fitness such as replicative capacity is weak; the correspondence with Potts model "prevalence"-based predictions is obscured by site conservation and limited precision. Double mutant cycles provide in principle one of the best ways to probe epistatic interactions experimentally without reference to a particular background, and we find they reveal that the most strongly interacting mutations in HIV involve correlated sets of drug-resistance-associated residues, however the analysis is complicated by the small dynamic range of measurements. The use of correlated models for the design of experiments to probe viral fitness can help identify the epistatic interactions involved in mutational escape, and lead to better inhibitor therapies. Author summaryProtein covariation models provide an alternative to experimental measures for estimating the fitness of mutations in proteins from across a variety of organisms. Yet, for viral proteins, it has been shown that models including epistatic couplings between residues, or other machine learning models perform no better or even worse than a simpler independent model devoid of such epistatic couplings in estimating viral fitness measurements such as replicative capacities, providing weak or ambiguous evidence for epistasis. We show that the evidence for long-range epistasis is strong by the analysis of the high-order marginals of the MSA distribution (up to subsequences of length 14), which are accurately captured by a correlated Potts sequence-covariation model but not by an independent model. While double mutant cycles in principle provide well-established biophysical probes for epistatic interactions, we demonstrate that the analysis and comparison between model and experiment is difficult due to the much smaller dynamic range of the measurements, making them more susceptible to noise.

biophysics↗

bioRxiv2021

Unique Features of Different Classes of G-Protein-Coupled Receptors Revealed from Sequence Coevolutionary and Structural Analysis

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and represent the primary targets of about one third of currently marketed drugs. Despite the critical importance, experimental structures have been determined for only a limited portion of GPCRs and functional mechanisms of GPCRs remain poorly understood. Here, we have constructed novel sequence coevolutionary models of the A and B classes of GPCRs and compared them with residue contact frequency maps generated with available experimental structures. Significant portions of structural residue contacts were successfully detected in the sequence-based covariational models. "Exception" residue contacts predicted from sequence coevolutionary models but not available structures added missing links that were important for GPCR activation and allosteric modulation. Moreover, we identified distinct residue contacts involving different sets of functional motifs for GPCR activation, such as the Na+ pocket, CWxP, DRY, PIF and NPxxY motifs in the class A and the HETx and PxxG motifs in the class B. Finally, we systematically uncovered critical residue contacts tuned by allosteric modulation in the two classes of GPCRs, including those from the activation motifs and particularly the extracellular and intracellular loops in class A GPCRs. These findings provide a promising framework for rational design of ligands to regulate GPCR activation and allosteric modulation. SignificanceG-protein-coupled receptors (GPCRs) play key roles in cellular signaling and serve as the primary targets of [~]1/3 of currently marketed drugs. In this work, we have presented the first analysis of both residue sequence coevolution and structural contact maps in different classes of GPCRs. We have inferred pathways for GPCR signal transduction that could not be determined from structural analysis alone. Distinct residue contacts have been identified in the signaling pathways of class A and B GPCRs. Our combined sequence coevolutionary and structural contact analysis has thus revealed important insights into the mechanism of GPCR signal transduction, which is expected to facilitate rational drug design of the GPCRs.

bioinformatics↗