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Chang L, Perez A. AlphaFold2 knows some protein folding principles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.25.609581. [PMID: 39253449 PMCID: PMC11383045 DOI: 10.1101/2024.08.25.609581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
AlphaFold2 (AF2) has revolutionized protein structure prediction. However, a common confusion lies in equating the protein structure prediction problem with the protein folding problem . The former provides a static structure, while the latter explains the dynamic folding pathway to that structure. We challenge the current status quo and advocate that AF2 has indeed learned some protein folding prin- ciples, despite being designed for structure prediction. AF2's high-dimensional parameters encode an imperfect biophysical scoring function. Typically, AF2 uses multiple sequence alignments (MSAs) to guide the search within a narrow re- gion of its learned surface. In our study, we operate AF2 without MSAs or initial templates, forcing it to sample its entire energy landscape - more akin to an ab initio approach. Among over 7,000 proteins, a fraction fold using sequence alone, highlighting the smoothness of AF2's learned energy surface. Additionally, by combining recycling and iterative predictions, we discover multiple AF2 interme- diate structures in good agreement with known experimental data. AF2 appears to follow a "local first, global later" folding mechanism. For designed proteins with more optimized local interactions, AF2's energy landscape is too smooth to detect intermediates even when it should. Our current work sheds new light on what AF2 has learned and opens exciting possibilities to advance our understanding of protein folding and for experimental discovery of folding intermediates.
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2
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Zhao K, Zhao P, Wang S, Xia Y, Zhang G. FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe. Genome Biol 2024; 25:152. [PMID: 38862984 PMCID: PMC11167914 DOI: 10.1186/s13059-024-03291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
Protein folding has become a tractable problem with the significant advances in deep learning-driven protein structure prediction. Here we propose FoldPAthreader, a protein folding pathway prediction method that uses a novel folding force field model by exploring the intrinsic relationship between protein evolution and folding from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring potential intermediates. On 30 example targets, FoldPAthreader successfully predicts 70% of the proteins whose folding pathway is consistent with biological experimental data.
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Affiliation(s)
- Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Pengxin Zhao
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Suhui Wang
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Yuhao Xia
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China.
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3
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Zhao W, Yang H, Cui H, Li W, Xing S, Han W. Elucidating the structural basis of vitamin B 12 derivatives as novel potent inhibitors of PTP1B: Insights from inhibitory mechanisms using Gaussian accelerated molecular dynamics (GaMD) and in vitro study. Int J Biol Macromol 2024; 268:131902. [PMID: 38692532 DOI: 10.1016/j.ijbiomac.2024.131902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024]
Abstract
Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 μM and 8.998 ± 0.587 μM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 μM and 4.04 μM respectively. To elucidate the inhibition mechanism, we conducted a 500 ns Gaussian accelerated molecular dynamics (GaMD) simulation. Utilizing PCA and tICA, we constructed Markov state models (MSM) to examine secondary structure changes during motion. Our findings revealed that the α-helix at residues 37-42 remained the most stable in the PTP1B-OHCbl Acetate system. Furthermore, upon binding of OHCbl Acetate or MeCbl, the WPD loop of PTP1B moved inward to the active pocket, forming a closed conformation and potentially obstructs substrate entry. Protein-ligand interaction analysis and MM-PBSA showed that OHCbl Acetate exhibited lower binding free energy and engaged in more residue interactions with PTP1B. In summary, our study confirmed the substantial inhibitory activity of OHCbl Acetate against PTP1B, with its inhibitory potency notably surpassing that of MeCbl. We demonstrated potential molecular mechanisms of OHCbl Acetate inhibiting PTP1B.
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Affiliation(s)
- Wencheng Zhao
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Hengzheng Yang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Huizi Cui
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Wannan Li
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
| | - Shu Xing
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
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Basu T, Das S, Majumdar S. Elucidating the influence of electrostatic force on the re-arrangement of H-bonds of protein polymers in the presence of salts. SOFT MATTER 2024; 20:2361-2373. [PMID: 38372459 DOI: 10.1039/d3sm01440a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Polyampholytes/proteins have an intriguing network of hydrogen bonds (H-bonds), especially their secondary structure, which plays a crucial role in determining the conformational stability of the polymer. The changes in protein secondary structure in the protein-salt system have been extensively deciphered by researchers, yet their pathways for breakage and recreation are unknown. Understanding the mechanism of protein conformational changes towards their biological activities, like protein folding, remains one of the main challenges and requires multiscale analysis of this strongly correlated system. Herein, salts have been used to reveal the re-arrangement behavior in the H-bond network of proteins under the influence of electrostatic interactions, as the strength of electrostatic forces is much stronger than that of H-bonds. At lower salt concentrations, there are negligible changes in the secondary structures as the electrostatic forces induced by the salt ions are less. Later, the existing H-bonds break and reconstruct new H-bonds at higher salt concentrations due to the influence of the stronger electrostatic interaction induced by the large number of salt ions. Molecular dynamics (MD) simulations and FTIR studies have been used rigorously to decipher the reason behind the re-arrangement of the H-bonds within gelatin (protein). The re-arrangement in the H-bond has also been observed with time from simulations and experiments. Thus, this study could provide a fresh perspective on the conformational changes of polyampholytes/proteins and will also influence the studies of protein folding-unfolding interaction in the presence of salt ions.
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Affiliation(s)
- Tithi Basu
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India.
| | - Sougat Das
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India.
| | - Saptarshi Majumdar
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India.
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5
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Jones SJ, Perez A. Molecular Modeling of Self-Assembling Peptides. ACS APPLIED BIO MATERIALS 2024; 7:543-552. [PMID: 36795608 DOI: 10.1021/acsabm.2c00921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Peptide epitopes mediate as many as 40% of protein-protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self- or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.
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Affiliation(s)
- Stephen J Jones
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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6
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Bhatia S, Udgaonkar JB. Understanding the heterogeneity intrinsic to protein folding. Curr Opin Struct Biol 2024; 84:102738. [PMID: 38041993 DOI: 10.1016/j.sbi.2023.102738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/09/2023] [Accepted: 11/10/2023] [Indexed: 12/04/2023]
Abstract
Relating the native fold of a protein to its amino acid sequence remains a fundamental problem in biology. While computer algorithms have demonstrated recently their prowess in predicting what structure a particular amino acid sequence will fold to, an understanding of how and why a specific protein fold is achieved remains elusive. A major challenge is to define the role of conformational heterogeneity during protein folding. Recent experimental studies, utilizing time-resolved FRET, hydrogen-exchange coupled to mass spectrometry, and single-molecule force spectroscopy, often in conjunction with simulation, have begun to reveal how conformational heterogeneity evolves during folding, and whether an intermediate ensemble of defined free energy consists of different sub-populations of molecules that may differ significantly in conformation, energy and entropy.
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Affiliation(s)
- Sandhya Bhatia
- Department of Biophysics, Howard Hughes Medical Institute UT Southwestern Medical Center, Dallas 75390, United States. https://twitter.com/Sandhyabhatia_5
| | - Jayant B Udgaonkar
- Department of Biology, Indian Institute of Science Education and Research Pune, Pashan, Pune 41008, India.
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7
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Caparotta M, Perez A. When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration. J Chem Theory Comput 2023; 19:8743-8750. [PMID: 38039424 DOI: 10.1021/acs.jctc.3c01019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2023]
Abstract
We introduce Gaussian accelerated MELD (GaMELD) as a new method for exploring the energy landscape of biomolecules. GaMELD combines the strengths of Gaussian accelerated molecular dynamics (GaMD) and modeling employing limited data (MELD) to navigate complex energy landscapes. MELD uses replica-exchange molecular simulations to integrate limited and uncertain data into simulations via Bayesian inference. MELD has been successfully applied to problems of structure prediction like protein folding and complex structure prediction. However, the computational cost for MELD simulations has limited its broader applicability. The synergy of GaMD and MELD surmounts this limitation efficiently sampling the energy landscape at a lower computational cost (reducing the computational cost by a factor of 2 to six). Effectively, GaMD is used to shift energy distributions along replicas to increase the overlap in energy distributions across replicas, facilitating a random walk in replica space. We tested GaMELD on a benchmark set of 12 small proteins that have been previously studied through MELD and conventional MD. GaMELD consistently achieves accurate predictions with fewer replicas. By increasing the efficacy of replica exchange, GaMELD effectively accelerates convergence in the conformational space, enabling improved sampling across a diverse set of systems.
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Affiliation(s)
- Marcelo Caparotta
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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Huang Z, Cui X, Xia Y, Zhao K, Zhang G. Pathfinder: Protein folding pathway prediction based on conformational sampling. PLoS Comput Biol 2023; 19:e1011438. [PMID: 37695768 PMCID: PMC10513300 DOI: 10.1371/journal.pcbi.1011438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/21/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023] Open
Abstract
The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than β-strands.
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Affiliation(s)
- Zhaohong Huang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xinyue Cui
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuhao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Mondal A, Lenz S, MacCallum JL, Perez A. Hybrid computational methods combining experimental information with molecular dynamics. Curr Opin Struct Biol 2023; 81:102609. [PMID: 37224642 DOI: 10.1016/j.sbi.2023.102609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023]
Abstract
A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.
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Affiliation(s)
- Arup Mondal
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK. https://twitter.com/@amondal_chem
| | - Stefan Lenz
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada. https://twitter.com/@jlmaccal
| | - Alberto Perez
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK.
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 79] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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11
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Maruyama Y, Igarashi R, Ushiku Y, Mitsutake A. Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation. J Chem Inf Model 2023; 63:1529-1541. [PMID: 36821519 PMCID: PMC10015464 DOI: 10.1021/acs.jcim.2c01444] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
We apply moving root-mean-square deviation (mRMSD), which does not require a reference structure, as a method for analyzing protein dynamics. This method can be used to calculate the root-mean-square deviation (RMSD) of structure between two specified time points and to analyze protein dynamics behavior through time series analysis. We applied this method to the Trp-cage trajectory calculated by the Anton supercomputer and found that it shows regions of stable states as well as the conventional RMSD. In addition, we extracted a characteristic structure in which the side chains of Asp1 and Arg16 form hydrogen bonds near the most stable structure of the Trp-cage. We also determined that ≥20 ns is an appropriate time interval to investigate protein dynamics using mRMSD. Applying this method to NuG2 protein, we found that mRMSD can be used to detect regions of metastable states in addition to the stable state. This method can be applied to molecular dynamics simulations of proteins whose stable structures are unknown.
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Affiliation(s)
- Yutaka Maruyama
- OMRON SINIC X Corporation, Tokyo 113-0033, Japan.,Department of Physics, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan
| | - Ryo Igarashi
- OMRON SINIC X Corporation, Tokyo 113-0033, Japan
| | | | - Ayori Mitsutake
- Department of Physics, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan
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Esmaeeli R, Bauzá A, Perez A. Structural predictions of protein-DNA binding: MELD-DNA. Nucleic Acids Res 2023; 51:1625-1636. [PMID: 36727436 PMCID: PMC9976882 DOI: 10.1093/nar/gkad013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/27/2022] [Accepted: 01/30/2023] [Indexed: 02/03/2023] Open
Abstract
Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).
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Affiliation(s)
- Reza Esmaeeli
- Department of Chemistry, Quantum theory project, University of Florida, Gainesville, FL 32611, USA
| | - Antonio Bauzá
- Department of Chemistry, Universitat de les Illes Balears, Palma de Mallorca (Baleares), 07122, Spain
| | - Alberto Perez
- Department of Chemistry, Quantum theory project, University of Florida, Gainesville, FL 32611, USA
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Chang L, Mondal A, Perez A. Towards rational computational peptide design. FRONTIERS IN BIOINFORMATICS 2022; 2:1046493. [PMID: 36338806 PMCID: PMC9634169 DOI: 10.3389/fbinf.2022.1046493] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022] Open
Abstract
Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry, University of Florida, Gainesville, FL, United States
- Quantum Theory Project, University of Florida, Gainesville, FL, United States
| | - Arup Mondal
- Department of Chemistry, University of Florida, Gainesville, FL, United States
- Quantum Theory Project, University of Florida, Gainesville, FL, United States
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, FL, United States
- Quantum Theory Project, University of Florida, Gainesville, FL, United States
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