Autoencoders for dimensionality reduction in molecular dynamics: Collective variable dimension, biasing, and transition states.

The heat shock protein 90 (Hsp90) is a molecular chaperone that controls the folding and activation of client proteins using the free energy of ATP hydrolysis. The Hsp90 active site is in its N-terminal domain (NTD). Our goal is to characterize the dynamics of NTD using an autoencoder-learned collective variable (CV) in conjunction with adaptive biasing force Langevin dynamics.

Surface ID: a geometry-aware system for protein molecular surface comparison.

Motivation: A protein can be represented in several forms, including its 1D sequence, 3D atom coordinates, and molecular surface. A protein surface contains rich structural and chemical features directly related to the protein's function such as its ability to interact with other molecules. While many methods have been developed for comparing the similarity of proteins using the sequence and structural representations, computational methods based on molecular surface representation are limited.

Isometric Stratified Ensembles: A Partial and Incremental Adaptive Applicability Domain and Consensus-Based Classification Strategy for Highly Imbalanced Data Sets with Application to Colloidal Aggregation.

Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection.

Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics.

The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times.

Scaffold-Constrained Molecular Generation.

One of the major applications of generative models for drug discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de novo drug design.

Protein loops with multiple meta-stable conformations: A challenge for sampling and scoring methods.

Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem.

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms.

Background: The existence of conformational changes in antibodies upon binding has been previously established. However, existing analyses focus on individual cases and no quantitative study provides a more global view of potential moves and repacking, especially on recent data. The present study focuses on analyzing the conformational changes in various antibodies upon binding, providing quantitative observations to be exploited for antibody-related modeling.

Differential Water Thermodynamics Determine PI3K-Beta/Delta Selectivity for Solvent-Exposed Ligand Modifications.

Phosphoinositide 3-kinases (PI3Ks) are involved in important cellular functions and represent desirable targets for drug discovery efforts, especially related to oncology; however, the four PI3K subtypes (α, β, γ, and δ) have highly similar binding sites, making the design of selective inhibitors challenging. A series of inhibitors with selectivity toward the β subtype over δ resulted in compound 3(S), which has entered a phase I/Ib clinical trial for patients with advanced PTEN-deficient cancer. Interestingly, X-ray crystallography revealed that the modifications making inhibitor 3(S) and related compounds selective toward the β-isoform do not interact directly with either PI3Kβ or PI3Kδ, thereby confounding rationalization of the SAR.

SAR156497, an exquisitely selective inhibitor of aurora kinases.

The Aurora family of serine/threonine kinases is essential for mitosis. Their crucial role in cell cycle regulation and aberrant expression in a broad range of malignancies have been demonstrated and have prompted intensive search for small molecule Aurora inhibitors. Indeed, over 10 of them have reached the clinic as potential anticancer therapies.

Cytoplasmic STAT3 represses autophagy by inhibiting PKR activity.

In a screen designed to identify novel inducers of autophagy, we discovered that STAT3 inhibitors potently stimulate the autophagic flux. Accordingly, genetic inhibition of STAT3 stimulated autophagy in vitro and in vivo, while overexpression of STAT3 variants, encompassing wild-type, nonphosphorylatable, and extranuclear STAT3, inhibited starvation-induced autophagy. The SH2 domain of STAT3 was found to interact with the catalytic domain of the eIF2α kinase 2 EIF2AK2, best known as protein kinase R (PKR).

Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol.

The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding.

Tricyclic series of heat shock protein 90 (Hsp90) inhibitors part I: discovery of tricyclic imidazo[4,5-c]pyridines as potent inhibitors of the Hsp90 molecular chaperone.

A novel class of heat shock protein 90 (Hsp90) inhibitors was developed after a low throughput screen (LTS) of a focused library containing approximately 21K compounds selected by virtual screening. The initial [1-{3-H-imidazo[4-5-c]pyridin-2-yl}-3,4-dihydro-2H-pyrido[2,1-a]isoindole-6-one] (1) compound showed moderate activity (IC(50) = 7.6 μM on Hsp82, the yeast homologue of Hsp90).

An electrostatic basis for the stability of thermophilic proteins.

Two factors provide key contributions to the stability of thermophilic proteins relative to their mesophilic homologues: electrostatic interactions of charged residues in the folded state and the dielectric response of the folded protein. The dielectric response for proteins in a "thermophilic series" globally modulates the thermal stability of its members, with the calculated dielectric constant for the protein increasing from mesophiles to hyperthermophiles. This variability results from differences in the distribution of charged residues on the surface of the protein, in agreement with structural and genetic observations.