Modeling side-chain conformation.

M Vásquez

doi:10.1016/s0959-440x(96)80077-7

Home/Archive/PubMed

PubMed · 8728654

Modeling side-chain conformation.

M Vásquez

View original source ↗Export metadata ↓

Abstract

Over the past few years, a number of methods for the calculation of side-chain conformations in proteins have been described. More recent studies have considered the effect of combinatorial packing, derivations from idealized rotameric structures and, to a limited extent, backbone flexibility on the quality and efficiency of calculations of protein side-chain conformation. Although further work is needed to address the issue of backbone displacements, the recent progress solves the packing problem to a significant degree. This opens the way for fruitful incorporation of these methods into general procedures for homology modeling and studies of ligand-protein interactions.

Explore related subjects

Computer Simulation Models, Molecular Protein Conformation Proteins Journal Article Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

M Vásquez. 1996. Modeling side-chain conformation.. https://doi.org/10.1016/s0959-440x(96)80077-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed1997

Structure and oxygen affinity of crystalline des-his-146beta human hemoglobin in the T state.

To correlate directly structure with function, the oxygen affinity and the three-dimensional structure of crystals of the T quaternary state of des-His-146beta human hemoglobin have been determined by polarized absorption microspectrophotometry and x-ray diffraction crystallography. In des-His-146beta, the COOH-terminal histidine residues of the beta chains of hemoglobin A have been removed. Oxygen binding to crystalline des-His hemoglobin is non-cooperative and independent of pH. The oxygen affinity is 1.7-fold greater than that of the crystalline state of hemoglobin A. Removal of His-146beta results in a small movement of the truncated COOH-terminal peptide and in a very small change in quaternary structure. Previously, similar studies on T state crystals of des-Arg-141alpha hemoglobin showed that removal of the COOH termini of the alpha chains results in much larger effects on oxygen affinity and on quaternary structure. Kinetic studies in solution reveal that at pH 7.0, the rates of CO combination with deoxygenated des-His-146beta in the absence and presence of inositol hexaphosphate are 2.5- and 1.3-fold, respectively, more rapid than for hemoglobin A. The values for des-Arg are 7.6- and 3.9-fold. The properties of the T state of hemoglobin both in the crystal and in solution are influenced to a greater degree by the interactions associated with Arg-141alpha than those associated with His-146beta.

Computer Simulation↗