A complete package for molecular mechanics, dynamics and modeling of molecules, especially biomacromolecules. TINKER has the ability to use any of several common parameter sets, such as Amber (ff94, ff96, ff98, ff99), CHARMM (19, 22 and 22-CMAP), Allinger MM (MM2-1991 and MM3-2000), OPLS (OPLS-UA, OPLS-AA and OPLS-AA/L), as well as our AMOEBA polarizable atomic multipole force field. TINKER implements a variety of novel algorithms including distance geometry with fast metrization and Gaussian trial distances, Elber's reaction path method, global optimization via our Potential Smoothing and Search algorithms, molecular dynamics with simulated annealing and stochastic dynamics options, particle mesh Ewald summation, Monte Carlo minimization, atomic multipole treatment of electrostatics with explicit dipole polarizability, Eisenberg-McLachlan ASP and GB/SA continuum solvation models, and truncated Newton TNCG local energy minimization.

A small set of programs that allow creation of PDB file summaries and searching of a dataset extracted from the PDB for structural motifs. For example, SLEUTH can find all examples of type I turns that match some generalized sequence pattern. An additional program implements the Eisenberg 3D profile algorithm. The /data subdirectory contains a set of summary files with dihedral angle values, secondary structure and solvent exposure data by residue for many of the current PDB structures.

A program to attack the "inverse" protein folding problem via modeling of the side chain packing in proteins. Allows the user to redesign a portion of a known structure using a library of side chain rotamers. Provides a list of the amino acid sequences that are predicted be compatable with an input tertiary fold. The PROPAK algorithm was one of the first attempts to attack this general protein design problem. The original program, although still useful, has not been under active development for some time and is largely superceded by methods from the labs of Steve Mayo, David Baker, and many others.

A program to derive QSAR models via regression analysis. The analysis can be performed by ordinary least squares (OLS), principal components (PCR) or partial least squares (PLS). "Leave-1-out" cross-validation is performed for all three types of analysis. For OLS and PCR, variable selection can be performed via simulated annealing on predictive power. In all cases, the final QSAR model can be used to make predictions for a test set distinct from the training set. Several training set data tables, including Garland Marshall's VALIDATE set for prediction of binding in macromolecular complexes, are provided as examples.