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Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication.

ABSTRACT: A protein structure is represented as a network of residues whereby edges are determined by intramolecular contacts. We introduce inhomogeneity into these networks by assigning each edge a weight that is determined by amino acid pair potentials. Two methodologies are utilized to calculate the average path lengths (APLs) between pairs: to minimize i), the maximum weight in the strong APL, and ii), the total weight in the weak APL. We systematically screen edges that have higher than a cutoff potential and calculate the shortest APLs in these reduced networks, while keeping chain connectivity. Therefore, perturbations introduced at a selected region of the residue network propagate to remote regions only along the nonscreened edges that retain their ability to disseminate the perturbation. The shortest APLs computed from the reduced homogeneous networks with only the strongest few nonbonded pairs closely reproduce the strong APLs from the weighted networks. The rate of change in the APL in the reduced residue network as compared to its randomly connected counterpart remains constant until a lower bound. Upon further link removal, this property shows an abrupt increase toward a random coil behavior. Under different perturbation scenarios, diverse optimal paths emerge for robust residue communication.

SUBMITTER: Atilgan AR

PROVIDER: S-EPMC1852335 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication.

Atilgan Ali Rana AR Turgut Deniz D Atilgan Canan C

Biophysical journal 20070209 9

A protein structure is represented as a network of residues whereby edges are determined by intramolecular contacts. We introduce inhomogeneity into these networks by assigning each edge a weight that is determined by amino acid pair potentials. Two methodologies are utilized to calculate the average path lengths (APLs) between pairs: to minimize i), the maximum weight in the strong APL, and ii), the total weight in the weak APL. We systematically screen edges that have higher than a cutoff pote ...[more]

PMID: 17293401

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Project description:The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing and retention-in-care rates would help inform implementation of relevant programs. Considering the dynamic and stochastic complexity of the disease and the time dynamics of decision-making, solving for optimal combinations using commonly used methods of parametric optimization or exhaustive evaluation of pre-selected options are infeasible. Reinforcement learning (RL), an artificial intelligence method, is ideal; however, training RL algorithms and ensuring convergence to optimality are computationally challenging for large-scale stochastic problems. We evaluate its feasibility in the context of the EHE goal. We trained an RL algorithm to identify a 'sequence' of combinations of HIV-testing and retention-in-care rates at 5-year intervals over 2015-2070 that optimally leads towards HIV elimination. We defined optimality as a sequence that maximizes quality-adjusted-life-years lived and minimizes HIV-testing and care-and-treatment costs. We show that solving for testing and retention-in-care rates through appropriate reformulation using proxy decision-metrics overcomes the computational challenges of RL. We used a stochastic agent-based simulation to train the RL algorithm. As there is variability in support-programs needed to address barriers to care-access, we evaluated the sensitivity of optimal decisions to three cost-functions. The model suggests to scale-up retention-in-care programs to achieve and maintain high annual retention-rates while initiating with a high testing-frequency but relaxing it over a 10-year period as incidence decreases. Results were mainly robust to the uncertainty in costs. However, testing and retention-in-care alone did not achieve the 2030 EHE targets, suggesting the need for additional interventions. The results from the model demonstrated convergence. RL is suitable for evaluating phased public health decisions for infectious disease control.

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Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication.

Publications

Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication.

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