pH-Dependent absorption spectrum of a protein: a minimal electrostatic model of Anabaena sensory rhodopsin.

A minimal electrostatic model is introduced which aims at reproducing and analyzing the visible-light absorption energy shift of a protein with pH. It relies on the existence of a protein structure, the prediction of titratable amino-acid pKa values and a very limited set of parameters. Applied to the case of the photochromic Anabaena sensory rhodopsin protein, the model succeeds in reproducing qualitatively the reported experimental data, confirming the importance of aspartic acid 217 in the observed blue shift in the λmax of ASR at neutral pH. It also suggests for the first time the role of two other amino acids, glutamic acid 36 at basic pH and aspartic acid 120 at acidic pH.

Toward Automatic Rhodopsin Modeling as a Tool for High-Throughput Computational Photobiology.

We report on a prototype protocol for the automatic and fast construction of congruous sets of QM/MM models of rhodopsin-like photoreceptors and of their mutants. In the present implementation the information required for the construction of each model is essentially a crystallographic structure or a comparative model complemented with information on the protonation state of ionizable side chains and distributions of external counterions. Starting with such information, a model formed by a fixed environment system, a flexible cavity system, and a chromophore system is automatically generated. The results of the predicted vertical excitation energy for 27 different rhodopsins including vertebrate, invertebrate, and microbial pigments indicate that such basic models could be employed for predicting trends in spectral changes and/or correlate the spectral changes with structural variations in large sets of proteins.

Molcas 8: New capabilities for multiconfigurational quantum chemical calculations across the periodic table.

In this report, we summarize and describe the recent unique updates and additions to the Molcas quantum chemistry program suite as contained in release version 8. These updates include natural and spin orbitals for studies of magnetic properties, local and linear scaling methods for the Douglas-Kroll-Hess transformation, the generalized active space concept in MCSCF methods, a combination of multiconfigurational wave functions with density functional theory in the MC-PDFT method, additional methods for computation of magnetic properties, methods for diabatization, analytical gradients of state average complete active space SCF in association with density fitting, methods for constrained fragment optimization, large-scale parallel multireference configuration interaction including analytic gradients via the interface to the Columbus package, and approximations of the CASPT2 method to be used for computations of large systems. In addition, the report includes the description of a computational machinery for nonlinear optical spectroscopy through an interface to the QM/MM package Cobramm. Further, a module to run molecular dynamics simulations is added, two surface hopping algorithms are included to enable nonadiabatic calculations, and the DQ method for diabatization is added. Finally, we report on the subject of improvements with respects to alternative file options and parallelization.

Constrained numerical gradients and composite gradients: Practical tools for geometry optimization and potential energy surface navigation.

A method is proposed to easily reduce the number of energy evaluations required to compute numerical gradients when constraints are imposed on the system, especially in connection with rigid fragment optimization. The method is based on the separation of the coordinate space into a constrained and an unconstrained space, and the numerical differentiation is done exclusively in the unconstrained space. The decrease in the number of energy calculations can be very important if the system is significantly constrained. The performance of the method is tested on systems that can be considered as composed of several rigid groups or molecules, and the results show that the error with respect to conventional optimizations is of the order of the convergence criteria. Comparison with another method designed for rigid fragment optimization proves the present method to be competitive. The proposed method can also be applied to combine numerical and analytical gradients computed at different theory levels, allowing an unconstrained optimization with numerical differentiation restricted to the most significant degrees of freedom. This approach can be a practical alternative when analytical gradients are not available at the desired computational level and full numerical differentiation is not affordable.