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      A Structure Independent Molecular Fragment Interfuse Model for Mesoscale Dissipative Particle Dynamics Simulation of Peptides

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      ACS Omega
      American Chemical Society

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          Abstract

          There is a need to develop robust computational models for mesoscale simulation of the structure of peptides over large length scales toward the discovery of novel peptides for medical applications to address the issues of peptide aggregation, enzymatic degradation, and short half-life. The primary objective was to predict the structure and conformation of peptides whose native structures are not known. This work presents a new model for computation of interaction parameters between the beads in coarse-grained dissipative particle dynamics (DPD) simulation that is properly calibrated for amino acids, supports compressibility requirement of water molecules, and accounts for subtle differences in the structure of amino acids and the charge in the side chain of charged amino acids. This new model is referred to as Structure Independent Molecular Fragment Interfuse Model, abbreviated as SIMFIM, because it accounts for specific interactions between different beads, which represent molecular fragments of the amino acids, in calculating nonbonded interaction parameters in the absence of knowing the actual peptide structure. The electrostatic interactions are incorporated in this model by using a normal distribution of charges around the center of the beads to prevent the collapse of oppositely charged soft beads. The uniquely parameterized DPD force field in the SIMFIM model is optimized for a given peptide with respect to the degree of coarse-grained graining for simulating the peptide over long times and length scales. The SIMFIM model was tested in this work using four peptides, namely, TrpZip2, Rubrivinodin, Lihuanodin, and IC3-CB1/Gai peptides, whose structures were sourced from the Protein Data Bank. The SIMFIM model predicted radius of gyration ( R g) values for the peptides closer to the actual structures as compared to the conventional model, and there was less deviation between the predicted and actual structures of the peptides.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The MARTINI force field: coarse grained model for biomolecular simulations.

            We present an improved and extended version of our coarse grained lipid model. The new version, coined the MARTINI force field, is parametrized in a systematic way, based on the reproduction of partitioning free energies between polar and apolar phases of a large number of chemical compounds. To reproduce the free energies of these chemical building blocks, the number of possible interaction levels of the coarse-grained sites has increased compared to those of the previous model. Application of the new model to lipid bilayers shows an improved behavior in terms of the stress profile across the bilayer and the tendency to form pores. An extension of the force field now also allows the simulation of planar (ring) compounds, including sterols. Application to a bilayer/cholesterol system at various concentrations shows the typical cholesterol condensation effect similar to that observed in all atom representations.
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              Statistical Mechanics of Dissipative Particle Dynamics

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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                12 April 2024
                23 April 2024
                : 9
                : 16
                : 18001-18022
                Affiliations
                [1]Biomimetic Materials and Tissue Engineering Laboratory, Chemical Engineering Department, University of South Carolina , 301 Main Street, Columbia, South Carolina 29208, United States
                Author notes
                [* ]Email: jabbari@ 123456cec.sc.edu . Tel: (803) 777-8022. Fax: (803) 777-0973.
                Author information
                https://orcid.org/0000-0001-6548-5422
                Article
                10.1021/acsomega.3c09534
                11044228
                b54687f2-678a-4b2b-8c66-29483c02e838
                © 2024 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 29 November 2023
                : 02 April 2024
                : 07 March 2024
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                ao3c09534

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